Applied Scientist jobs in Tel Aviv-Yafo

Overview Microsoft Teams is the hub for teamwork that integrates all the people, content, and tools your team needs to be more engaged and effective. It is core to Microsoft’s modern work, modern life & modern education value prop. We are reinventing the way people communicate and work together across the globe.   We are looking to hire a PhD (or published MSc) candidate for a 12-week, full-time internship to join CMD Labs – an applied science team within Microsoft Teams – to work on improving collaborative agents by applying existing or novel research. Our flagship AI application for Teams is the Copilot agent that works in multi-party collaborative settings (meetings, group chats, channels) and enables groups of humans and agents to more efficiently achieve their shared goals. While most human/agent interactions are textual, within live Teams meetings the agent also interacts in real time via speech. Unlike personal agents (both audio and text), multi-party collaborative agents are faced with significant challenges in implicit context understanding, proactivity, and conversational capabilities. The intern will be onboarded to our current research directions, and to our non-confidential datasets and related code, and work on improving the existing algorithms as well as proposing novel solutions, in any of the following topics: • Training/fine-tuning speech-enabled models (e.g. Qwen-omni-8) to support speech in multi-party settings such as business meetings. • Improve and devise new methodologies for evaluation of audio/text collaborative agents. • Improve and devise better RL recipes for training/fine-tuning collaborative models including synthetic data generation, and the development and calibration of graders. Given substantial scientific novelty of the approach and results, collaboration on a mutual publication is encouraged. Responsibilities • Conduct experiments, create and validate metrics, and develop candidate algorithms to improve the existing capabilities of current models and agent implementations in any of the above topics. • Collaborate closely with CMD Labs researchers and engineers to leverage existing assets, datasets, and ensure results can contribute to the product. • Embody our culture and values. Qualifications Required • Currently enrolled in a PhD program (or published candidate in MSc program) in Computer Science, Electrical or Computer Engineering, Statistics, or a related field. • Practical experience in training, fine-tuning, and prompt engineering of transformer models or LLMs. • Practical Python coding experience leveraging modern coding agents. Preferred     • Field of research and publications directly related to Audio LLMs or collaborative models and agents. *** Please note that this is a 12 weeks full time intenship ****** 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 About Us: Zenity is the leader in AI Agent Security and the first company to bring an agent-centric security platform to market. As enterprises accelerate AI agent adoption, we are establishing the security framework for how AI agents are secured and governed at enterprise scale. We deliver full-lifecycle visibility, governance, detection, prevention, and response for AI agents from build time to runtime, across SaaS, home-grown platforms, and end-user devices. Backed by $180M+ in total funding, including a $125M Series C led by Norwest, with participation from SoftBank Vision Fund 2 and Microsoft's M12, Zenity is trusted by Fortune 500 and Global 2000 enterprises worldwide. Join us in shaping how AI agents are secured at enterprise scale. About the Role: We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies. You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you. What You’ll Do Responsibilities: • Build, train, and ship detection models end-to-end, from raw data to production • Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results. • Partner with security researchers to turn security research outputs and domain expertise into detection capabilities • Own evaluation: design benchmarks, build labeled datasets, and define production standards • Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability • Iterate fast, with a tight feedback loop between model performance and product outcomes Requirements Requirements: • 5 years of hands-on ML and deep learning experience, with a track record of shipping, debugging, and diagnosing models in production • Data-first mindset: you know how to define the right evaluation criteria for each model - before and after shipping, to ensure it delivers real quality and value in production • Hands-on experience building and deploying agentic AI systems to production • Proficiency in Python; experience with PyTorch, scikit-learn, HuggingFace, or equivalent • Practical, applied mindset - focused on the problem, success metrics and impact, not lab research. • • Background in security, trust & safety, or content moderation - an advantage

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities • Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. • Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. • Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. • Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. • Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. • Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. • Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. • Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves • A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. • A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. • Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. • Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. • Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. • Excellent written and verbal communication. Nice-to-haves • First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. • Experience deploying ML models or inference optimizations in production. • Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. • Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. • Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. • Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: • Competitive compensation • Career growth and learning opportunities • Flexibility and ownership • Collaborative and innovative culture • Opportunity to work on impactful AI projects • International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.