Data Scientist jobs in Israel

Description Applied Data Scientist — Intent Drift Detection Backslash Security · Realtime AI-agent protection About Backslash We are builders who want to secure the next generation of software development from the inside out. Software development has entered a new era defined by vibe-coding, MCP servers, and AI agents. While these tools allow developers to build faster than ever, they also introduce a completely new frontier of security challenges that exist directly on the engineer's machine. Backslash is building the security layer for this new reality. We provide the essential guardrails and visibility needed to secure AI-driven workflows. By actively protecting developer computers and monitoring IDE-based AI interactions, we ensure the future of coding is as secure as it is fast. The Role AI coding agents run with enormous privilege on developer machines. When untrusted content steers one off its task, classical security sees only the final action — never that the agent's intent drifted. Detecting that drift, in realtime, on-device, is an open problem. No one has solved it. You will. You will own intent-drift detection end-to-end — from the data to the model: • Build the Dataset: Design and generate the data that defines what "drift" looks like, and co-design the pipeline that creates it. • Train Small, Ship Local: Fine-tune, distill, and shrink the models that detect drift in realtime — small enough to run fully on-device, no cloud. • Shape the Architecture: Decide what's a model, what's a rule, and what's a gate. The detector's design is your call. • Measure What Matters: Build the evaluation harness — realtime detection at very low false-positive rates on a developer's machine is the product. You own intent-drift analysis as a problem, not a ticket. Requirements • Applied Data Science End-to-End: 3–5 years turning ambiguous problems into working models — problem framing, data collection and labeling strategy, feature/representation design, training, and evaluation. You've owned a dataset and the model that consumed it. • Small-Model Fluency: Comfortable fine-tuning small open models locally — DeBERTa-class encoders, ≤8B decoders, LoRA, quantization, single-GPU workflows. • Applied-Research Mindset: You approach an open research problem with passion and a practical, ship-it attitude. • Independence: You want an impactful, strategic problem in LLM security — not a ticket queue. • Startup DNA: Can-do, fast, a true partner. Non-negotiable. Local-first by design. Python, PyTorch, HuggingFace, ≤8B models. Bonus Points For • Security Background: Prompt injection, MITRE ATLAS, agent security — or the hunger to learn it fast. • Adversarial ML: Experience with LLM evaluation, red-teaming, or building adversarial datasets. • Endpoint Software: Familiarity with software that runs locally on developer machines with a low resource footprint.

Overview Come build community, explore your passions and do your best work at Microsoft with thousands of University interns from every corner of the world. This opportunity will allow you to bring your aspirations, talent, potential—and excitement for the journey ahead.   As a Data Scientist Intern at Microsoft, you will combine applied AI, engineering, and scientific experimentation to build intelligent systems. Your work will focus on developing autonomous AI agents, evaluating large language models (LLMs), and automating research workflows. You will design experiments and apply statistical methods to assess and improve AI systems, accounting for the variability and uncertainty inherent in their non-deterministic behavior. Working with researchers, engineers, product teams, and other stakeholders, you will translate customer and business needs into practical solutions and measurable outcomes. You will combine analytical rigor with hands-on development to build prototypes and evaluation frameworks that improve the quality, reliability, and usefulness of AI-powered experiences. At Microsoft, Interns work on real-world projects in collaboration with teams across the world, while having fun along the way. You’ll be empowered to build community, explore your passions and achieve your goals. This is your chance to bring your solutions and ideas to life while working on cutting-edge technology. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities • Develop and improve autonomous AI agents and research workflow automations using LLMs, relevant algorithms, and appropriate data sources. • Explore emerging AI techniques, propose new ideas, and build proofs of concept (POCs) to assess their feasibility, potential value, and limitations. • Design experiments and evaluation frameworks for LLMs and AI agents, applying statistical methods to measure quality and reliability while accounting for non-deterministic behavior. • Explore data, analyze experimental results, and investigate failure modes to identify opportunities, validate approaches, and iteratively improve AI systems. • Communicate findings, limitations, and technical trade-offs clearly, providing actionable insights that inform product and engineering decisions. • Collaborate with researchers, engineers, product teams, and other stakeholders to translate customer and business needs into opportunities to enhance products and customer experiences through applied AI. **This student position is structured as a one-and-a-half-year role, concluding upon completion of the academic degree. Qualifications • Currently pursuing a Master’s degree in Data Science related fields:, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field. • At least 3 additional semesters before graduation – graduation date Winter 2028 or later. • knowledge of or experience working with Large Language Models (LLMs), including areas such as fine-tuning, modeling, evaluation, frontier models, or specialized models. • Hands-on experience writing code in a production environment and working with code repositories (e.g., Git). Preferred Qualifications: • Currently pursuing a PhD in Data Science related fields: Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Overview The WSSI team is looking for a motivated Data Science Intern to contribute to production-oriented AI solutions across computer vision and efficient small language models, helping bring advanced models to Windows products under real-world edge-device constraints.The team’s primary focus is developing state-of-the-art computer vision solutions that can run efficiently under constraints such as model size, latency, power, and deployment efficiency. In this internship, you will work with applied scientists, engineers, MLOps specialists, and data experts to help turn applied research and prototypes into high-quality AI experiences that can create impact for millions of customers. Responsibilities • Contribute to research, training, experimentation, evaluation, and prototyping of AI models in computer vision, efficient small language models, or related applied AI areas. • Support the development of production-oriented AI solutions, with attention to model quality, reliability, scalability, and measurable customer impact. • Analyze model behavior and performance under real-world edge-device constraints, including latency, footprint, power, and resource usage. • Contribute to exploring model optimization techniques including compression and efficient inference to enable deployment in resource constrained environments. • Work with datasets, evaluation pipelines, and experiment tracking to generate clear insights and support data-driven decisions. • Collaborate closely with product, system engineering, software, and silicon teams, and clearly communicate findings, tradeoffs, and recommendations. Qualifications Required Qualifications: • Currently pursuing an M.Sc. or Ph.D. in Computer Science, Electrical Engineering, Data Science, or a related technical field. • Hands-on experience with model development, evaluation, experimentation, and data analysis. • Practical Python development experience, including the use of modern AI-assisted coding tools. Preferred Qualifications: • Experience with computer vision systems, image/video processing, or AI models designed for real-world deployment. • Experience with model optimization, compression, quantization, distillation, efficient inference, or deployment on edge devices. • Experience working with data pipelines, model lifecycle workflows, MLOps concepts, or production AI infrastructure. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Description Lumenis is a global leader in energy-based solutions for Aesthetics and Eye Care. Headquartered in Yokneam, Israel, with offices in Tel Aviv, Lumenis has a strong global presence and business performance worldwide. We are looking for a Data Insights & Analytics Manager who will develop a deep understanding of data generated by Lumenis' global installed base of connected medical devices, combine it with enterprise data, and translate it into insights and tools that create value for our customers and the business. The role goes beyond reporting and dashboard creation. We are looking for someone who is naturally curious, proactively explores data, identifies patterns and opportunities, and drives the organization to act on what the data reveals. Importantly, the role is expected to uncover broader customer, commercial, and business insights by connecting IoT data with enterprise and other relevant data sources - not only insights derived from device data itself. Main Duties: This is a highly hands-on analytics role at the intersection of data, technology and business, requiring both strong analytical capabilities and a product mindset. Data Foundation & BI • Develop a deep understanding of Lumenis’ IoT data ecosystem, including device events, data structures, business logic, and KPIs. • Work hands-on with data across AWS and enterprise systems. • Partner with BI to define requirements, maintain trusted metrics and models, and improve data quality and usability. • Independently build, maintain, and enhance dashboards and analytics solutions in Amazon QuickSight. Analytics & Insights • You will be expected not only to answer business questions with data, but to identify the questions we should be asking. • Proactively explore complex IoT and business datasets to uncover meaningful trends, patterns, anomalies, and opportunities. • Translate complex data into clear, actionable insights and proactively bring relevant findings to the business. Business & Commercial Analytics • Partner with commercial teams to improve customer engagement, strengthen sales narratives, and develop data-driven tools. • Create compelling, evidence-based data stories for internal and external audiences. • Measure the impact of data-driven initiatives and refine them based on results. Data Activation & Adoption • Drive adoption of analytics tools and data-driven practices across the organization. • Train business users to interpret and apply data effectively. • Proactively communicate relevant insights rather than relying solely on self-service dashboards. • Continuously identify new ways data can create value for customers and the business. Key Interfaces • BI; Sales and Commercial Excellence; Marketing; Service; Product; Finance and Operations; and regional business teams. Requirements • Significant experience in BI, business analytics, data analytics, or a similar role. • Strong SQL skills and hands-on experience with BI platforms such as QuickSight, Power BI, or Qlik. • Ability to independently explore complex datasets and understand data structures, business logic, and KPI definitions. • Strong analytical curiosity, business acumen, and a track record of translating insights into commercial opportunities and actions. • Excellent data visualization, storytelling, communication, and stakeholder-management skills. • A proactive, independent approach, with the ability to drive adoption and influence without formal authority. • Additional advantages include experience with: • IoT, connected-device, or product-usage data. • AWS, S3, Salesforce, SAP, or Python. • Medical devices, healthcare, SaaS, or another connected installed-base business. • Customer-engagement or commercial analytics in a global organization.

Description We're looking for a curious and motivated Data Scientist to join our Data & AI team and become the company's future in-house expert on our AI platforms and solutions. Reporting to the Tech Lead, Data & AI, you'll work closely with external development teams, gain deep understanding of the system architecture, support data analysis and model evaluation, and help maintain and improve production solutions over time. Requirements • 1-3 years of experience in Data Science, AI, Software Engineering, or a related field. • Strong Python and data analysis skills. • Fast learner with strong analytical thinking and a desire to grow. • Relevant degree in Computer Science, Engineering, Mathematics, or similar. • Fluent English. Advantage • Experience with LLMs, NLP, Search, or open-source AI models. • Background in defense or intelligence environments. • Active security clearance. This is an excellent opportunity for an early-career professional to learn complex AI systems end-to-end, work alongside experienced experts, and quickly grow into a key technical contributor.

Description EverC, now part of G2 Risk Solutions, is a pioneer in its field. By leveraging technologies that illuminate the darkest corners of the internet and provide unparalleled visibility into the largest source of data in the world, EverC uses artificial intelligence and machine learning techniques to assess hundreds of millions of domains and effectively categorize the internet. These insights reveal hidden relationships and risks, identify an entity’s full digital fingerprint, and create new opportunities for businesses to scale efficiently and confidently. About the Role We’re looking for a hands-on Data Scientist who owns AI and LLM-powered products end to end — and cares about how they perform for real users. You will own the modeling and evaluation of our fraud and risk detection products: exploring the data, designing models and prompts, evaluating them offline, and monitoring them in production. You’ll work side by side with engineers to ship your models into large-scale, real-time systems — you drive the data science and the product outcomes, while engineering owns the platform plumbing. This is a data science role at heart. You won’t be a data engineer, but you’ll be comfortable working close to the data and the systems, and knowing your way around modern ML and data infrastructure is a strong plus. Responsibilities Data Science & Modeling • Own the design, build, and evaluation of LLM-based and classical ML models for classification, entity extraction, and risk scoring on large-scale, real-world data. • Lead prompt design and iteration — model configurations, fallback strategies, and tradeoffs between cost, latency, and quality. • Apply sound statistical judgment on adversarial, noisy, or imperfectly labeled data. • Drive the modeling decisions behind how LLMs and ML models fit into streaming, event-driven production systems. Product & Platform Partnership • Partner with engineers to ship your models into real-time streaming and large-scale batch pipelines — you own the modeling, they own the platform. • Shape internal evaluation tooling, LLM observability, and model-performance monitoring. • Define the requirements your infrastructure must meet to support rapid experimentation without hurting production reliability. Data Quality & Product Performance • Own the data-quality and product metrics that matter (precision, recall, coverage, latency, cost efficiency). • Build measurement frameworks for production systems and offline experiments. • Analyze production data to find labeling gaps, false-positive patterns, and new detection opportunities. • Drive experimentation (A/B tests, shadow deployments, offline evaluation) and back every decision with measurable business outcomes. Requirements • 5+ years applied data science / ML, including production model deployment. • Hands-on LLM prompt engineering and evaluation (not just fine-tuning theory). • Strong Python. • Experience with large-scale data processing concepts and technologies. • Strong grasp of data-intensive applications and modern data architectures. • Experience building cloud-native systems (AWS preferred). • Excellent communication and stakeholder management. • Comfortable with ambiguous, adversarial data and designing labels/heuristics where ground truth is imperfect. Preferred Qualifications A data scientist who is also fluent in the ML and data-engineering stack is a big plus: • Data & Infrastructure: Apache Spark, Kafka, Kubernetes, Docker, EMR, Airflow, Iceberg / Delta Lake, K8s • Cloud & DevOps: Infrastructure as Code (Terraform), CI/CD (GitHub Actions), monitoring & observability platforms • AI & ML: ML platforms, LLM-powered applications, vector databases

Description Pango is looking for an experienced Data Scientist to join us and take a key role in turning large-scale, real-world data into smarter product and business decisions.With millions of users interacting with Pango's mobility services, our data reflects complex customer behavior, business models, and real-life mobility patterns.We're looking for someone who can go beyond building models — someone who knows how to identify the right problems, challenge assumptions, uncover opportunities in the data, and translate them into real business impact. You'll have significant professional ownership, from taking an ambiguous business question and framing it as a statistical or Machine Learning problem, through choosing the right methodology, building and evaluating the solution, and bringing it to production.Just as importantly, we expect you to proactively identify questions that should be answered with data — even when nobody has asked them yet. Responsibilities • Own Data Science problems end-to-end — from business question and hypothesis to modeling, evaluation, and production. • Partner closely with Product, Business, Data, and Engineering teams to translate complex business challenges into data-driven solutions. • Proactively identify opportunities where statistical analysis, Machine Learning, and AI can improve our products, customer experience, and business performance. • Build, train, evaluate, and improve Machine Learning models used in production, across both real-time and batch use cases. • Select the right statistical or ML approach based on the problem and the data — including recognizing when a simple analytical solution is better than a complex model. • Define meaningful evaluation methodologies and connect model performance to real business impact. • Communicate complex statistical findings clearly to both technical and non-technical stakeholders. Requirements • 4–6 years of hands-on experience as a Data Scientist. • Strong statistical foundation and hands-on experience with Machine Learning. • Strong experience with Gradient Boosted Trees, such as XGBoost, LightGBM, or CatBoost. • Hands-on experience with AI technologies, including Generative AI and foundation models, as part of real-world solutions. • Strong Python and SQL skills. • Experience developing and deploying models in a production environment. • B.Sc. or M.Sc. in Statistics, Applied Mathematics, Physics, Electrical Engineering, Computer Science, or a related quantitative field. Advantages • Experience with production ML platforms such as AWS SageMaker. • Experience with real-time and batch inference. • Experience with neural networks, sequence models, and embeddings. • Experience working in a large-scale B2C or data-rich product environment.

Description A growing B2B SaaS company is looking for an experienced Head of Data Science to lead its Data Science function and drive the next generation of AI and machine learning capabilities. This is a strategic leadership role combining people management, hands-on problem-solving, cross-functional collaboration, and ownership of data-driven innovation across the organization. Responsibilities • Lead and mentor a global team of Data Scientists • Define and execute the company's Data Science strategy • Drive the development of advanced machine learning, optimization, and forecasting solutions • Partner closely with Product, Engineering, Customer Success, and executive leadership • Translate complex business challenges into scalable analytical solutions • Support customer-facing initiatives and strategic engagements when needed • Establish best practices for experimentation, model evaluation, and performance measurement • Act as a key stakeholder in shaping the company's AI vision and roadmap Requirements • 8+ years of experience in Data Science, Machine Learning, Statistics, Operations Research, Economics, or a related field • Proven experience leading Data Science teams • M.Sc. or Ph.D. in a quantitative discipline • Strong expertise in machine learning, predictive analytics, optimization, forecasting, and statistical modeling • Experience in fintech, revenue optimization, forecasting, recommendation systems, retail technology, or similar data-intensive domains • Strong business orientation and ability to connect analytical insights to business outcomes • Excellent communication and stakeholder management skills • Experience working in a SaaS environment • Fluent English Advantage • Experience with Generative AI and LLM-based applications • Experience working with enterprise customers • Experience managing distributed teams

Description Join Noma Security and help shape the future of AI security in a fast-paced, high-impact environment. Noma is building the first comprehensive AI Security and Governance Platform, helping organizations adopt AI and LLMs with confidence and control. As AI becomes core to modern enterprises, we provide seamless visibility and protection across the entire AI lifecycle. Our team works at the intersection of AI, security, and cloud, shaping a new category in a rapidly evolving market. At Noma, you’ll have the opportunity to make a meaningful impact while working with exceptional people on technology that truly matters. We’re looking for a Senior Data Scientist to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights. In this role, you will design, build, and productionize advanced models that help organizations secure AI systems across data, models, and runtime usage. You’ll work closely with Product, Engineering, and Security teams to turn complex, real-world security problems into scalable, high-impact AI solutions. This is a high-impact, hands-on role with true ownership, where your work directly shapes the intelligence powering a category-defining AI Security platform. 🛠️ What You’ll Do: • Own End-to-End Model Development (Research → Production) • Take full ownership of the model lifecycle: problem definition, research, prototyping, training, evaluation, deployment, and ongoing optimization. • Design and build scalable ML / AI solutions to detect, analyze, and mitigate AI-related security risks. • Ensure models are robust, explainable, and production-ready in real-world enterprise environments. • Partner Deeply with Product & Engineering • Collaborate closely with Product Managers to translate customer pain points and security requirements into clear modeling objectives. • Work hands-on with engineering to integrate models into production systems and data pipelines. • Balance research depth with pragmatic execution and business impact. • Build AI Security Intelligence • Develop capabilities across areas such as AI behavior analysis, anomaly detection, model governance, data risk detection, and usage observability. • Work with structured and unstructured data, including logs, prompts, model outputs, and metadata. • Continuously iterate on models based on usage patterns, feedback, and emerging threat vectors. • Lead Research POCs & Innovation • Stay at the forefront of LLMs, applied ML, and AI security techniques and bring new ideas into the product. ✅ What You Bring: • 5+ years of experience in Data Science or Applied ML roles. • 3+ years of backend development experience, working with production systems. • Strong hands-on experience with language models / LLMs and modern ML techniques. • Bachelor’s degree in Computer Science (or equivalent practical experience). • Proven experience owning end-to-end research POCs, from ideation to real-world impact. • Strong understanding of product and business value, with a bias toward lean, effective solutions. • Deep understanding of language models 🎯 Nice to Haves • Experience in AI security, application security, or threat detection. • Background in LLM workflows, RAG pipelines, prompt analysis, or multi-model systems. • Experience building internal platforms or developer-facing ML tooling. • Previous experience in leadership roles such as Tech Lead or Team Lead. • Startup experience, especially in early or growth-stage companies. 💙 Perks & Benefits • Located in a prime area with easy access to public transportation, near the Hashalom train station and the Light Rail. • A bright, spacious, dog-friendly office that makes you smile the moment you walk in 🐶 • Team bonding activities, company retreats, happy hours, and holiday gifts • Pilates classes at the office 💪🧘‍♀️ • Never hungry: Monthly lunch budget, dinners provided, and a fully stocked kitchen 🍽️ • A warm, supportive, and people-first company culture • Pension & Keren Hishtalmut (advanced training fund) • A global work environment with teams across Tel Aviv, the US, the Netherlands, and London • Options that let you share in Noma’s growth and success About The Company Noma is building the industry's first application security platform built to secure the Data & AI Lifecycle—from development to production and from classic data pipelines and ML to GenAI. Our mission is to create a common language between data science and application security teams, helping them secure AI-powered apps without slowing down AI innovation. In just a year, we've helped our customers, including Fortune 500 companies, secure and protect their entire Data & AI Lifecycle with our holistic platform.

Description Vega is one of the fastest-growing startups in cybersecurity, redefining security analytics and operations with an AI-native platform for the SOC. We are building the next-generation operating system for security teams. Vega is already delivering real impact at some of the world’s largest organizations - improving detection, unlocking the value of their security data, and reducing cost and complexity. With HQs in New York and TLV, we're looking for people who want to be a part of the next rocket-ship in cyber. We're looking for a Data Scientist to join our team. As a Data Scientist at Vega, you’ll be a key member of our AI team, designing, building, and deploying cutting-edge AI systems that leverage large language models (LLMs), unstructured data, and advanced ML techniques. You’ll be instrumental in bringing AI from research to real-world production, delivering high-impact features that drive our security platform. In this role, you'll have a unique opportunity to solve complex problems, work with large scale data and be part of the core team that shapes the future of AI in the company. WHAT YOU WILL DO • Design and develop end-to-end AI solutions, from data ingestion and modeling to deployment and observability. • Build and fine-tune LLM-based applications to tackle complex cybersecurity challenges. • Own the full ML lifecycle — from ideation and experimentation to production readiness. • Collaborate with product, engineering, and security teams to turn AI research into user-facing features. • Work with large-scale unstructured data (e.g., logs, threat intel, alerts) to extract meaningful insights. • Evaluate and optimize AI system performance, scalability, and reliability. • Contribute to core architectural decisions related to AI and ML infrastructure. Requirements WHAT YOU WILL BRING • 4+ years of professional experience in ML/AI engineering. • Hands-on experience working with LLMs and building AI agents in production environments. • Strong understanding of text classification, ranking, and evaluation in NLP systems. • Proficiency in Python and modern ML frameworks. • Deep knowledge of deploying and maintaining AI systems at scale. • Experience with cloud platforms, particularly AWS. • Experience working independently in fast-paced, mission-driven environments • Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field NICE TO HAVE • Experience fine-tuning LLMs for domain-specific or task-specific use cases. • Background in cybersecurity applications (e.g., threat detection, incident response, log analysis). • Familiarity with agent-based architectures and autonomous systems.

Job Description Armis from ServiceNow, protects the entire attack surface and manages an organization’s cyber risk exposure in real time. Combined with ServiceNow’s Security and Risk capabilities, as well as Identity Security capabilities from Veza, we bring together all critical capabilities to secure every asset, every identity - incl. AI Agents - across all environments with the right level of context - security, operational and business - to build the first of its kind autonomous defense platform for the Agentic Enterprise. As an AI-driven cybersecurity company, Machine Learning and Generative AI power the core capabilities that make Armis an industry leader. Our algorithms are the engine behind our advanced Vulnerability Management, Detection, and Remediation (VMDR) platform. We are looking for a Staff Data Scientist with strong Machine Learning foundations and practical enterprise experience to join the VMDR team. In this role, you will act as a technical builder owning complex problem domains end-to-end, diving deep into cybersecurity nuances, and architecting cutting-edge LLM and ML solutions from initial research all the way to scalable production deployment. What You’ll Do • End-to-End Ownership: Drive complex AI and ML initiatives through the full product lifecycle from early-stage prototyping and deep domain analysis to production-grade deployment and monitoring. • Architect & Deploy: Design and build production-ready AI models and system pipelines in Python using modern cloud and big data infrastructure (AWS, Spark). • LLM Innovation: Integrate state-of-the-art Generative AI and LLM workflows directly into our core VMDR features to drive automated detection, prioritization, and remediation insights. • Domain Deep-Dives: Partner closely with domain researchers, data engineers, and product leaders to dive into granular cybersecurity data and translate complex security problems into high-impact AI solutions. Qualifications To be successful in this role you have: • Cybersecurity Background: Domain experience in cybersecurity (specifically Vulnerability Management, Asset Intelligence, or Threat Detection) is a big advantage, but not mandatory. • LLM Expertise: At least 2 years of hands-on experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, RAG, agentic workflows, and orchestration frameworks (e.g., LangChain, LlamaIndex, vLLM, or similar). • Solid ML Foundations: Deep algorithmic and theoretical knowledge in Machine Learning, supported by an Academic Degree (M.Sc. or Ph.D. preferred) or equivalent research experience. • Production Engineering Skills: 5+ years of software development experience, with strong, production-grade Python coding abilities and experience deploying ML systems at scale. • Self-Driven & Collaborative: An autodidact who naturally takes full ownership of problem domains, stays ahead of emerging AI trends, and thrives in a high-trust, collaborative team environment. Additional Information Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.  Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.