Machine Learning Engineer jobs in Israel

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re looking for a hands-on ML Ops Engineer to partner with our data scientists to turn their models into production-ready systems. In this role, you’ll report to the Data Science Manager and work closely with our Data Scientists and Product developers. You’ll architect storage and compute, harden training/inference pipelines, and make our ML code, data workflows, and services reliable, reproducible, observable, and cost-efficient. You’ll also set best practices and help scale our platform as Nift grows. Our Mission: Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded. We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by Spark Capital & Foundry who also invested in Slack, Snap, SeatGeek, Fitbit, Warby Parker, Wayfair and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth here. What you will do: • ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance • Feature & model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows • Observability & quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards • Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility • Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance • Roadmap & standards: Drive the technical vision for ML platform capabilities and establish architectural patterns that become team standards What you need: • Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systems • Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds. Extensive commercial experience with Python developing automated pipelines bringing ML models to production • Cloud & containers: Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent) • IaC: Terraform or CloudFormation for managed, reviewable environments • ML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelines • Monitoring/observability: ML monitoring (quality, drift, performance) and pipeline alerting • Collaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup • PySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processing • Analytics platforms: Experience integrating 3rd party data • Model serving patterns: Familiarity with real-time endpoints, batch scoring, and feature stores • Governance & security: Exposure to model governance/compliance and secure ML operations • Be mission-oriented: Proactive and self-driven with a strong sense of initiative; takes ownership, goes beyond expectations, and does what's needed to get the job done What you get: • Competitive compensation, flexible remote work • Unlimited Responsible PTO • Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI. Key responsibilities • Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence • Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives • Establish best practices, standards, and processes for ML infrastructure, deployment, and operations • Own technical decision-making for ML infrastructure architecture and tooling choices • Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream • Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions • Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data • Collaborate with research teams to bridge the gap between experimentation and production • Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability Must have • Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience) • 7+ years of experience in MLOps, with 5+ years in a technical leadership role • Strong software engineering skills in Python, with experience in Bash and/or Go • Proven track record of building and leading high-performing MLOps or infrastructure teams • Experience building and designing MLOps infrastructure from the ground up • Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.) • Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference • Experience building and managing data pipelines to support both model training and inference • Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps) • Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry) • Excellent communication skills with ability to translate between research and production contexts Nice to have • Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows) • Experience with FastAPI and backend applications • Familiarity with data platforms like Databricks or Snowflake • Experience with LLM/foundation model serving and optimization • Exposure to SRE practices or cloud security certifications • Experience scaling ML infrastructure for AI startups Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys • Relocation support for employees moving to join the team in one of our office locations • A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI. Key responsibilities • Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc. • Develop scalable inference architectures optimized, with ultra-low latency and high throughput • Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities • Develop logging, alerting, and monitoring solutions to track model development, and reliability • Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency • Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data Must have • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience) • 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..) • Experience building and designing MLOps infrastructure from the ground up • Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference • Experience in building and managing data pipelines to support both model training and inference • Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps) • Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code • Experience in AI/ML systems security, compliance, and model governance • Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry Nice to have • Experience with ML workflow tooling (MLflow, Kubeflow, or similar) • Experience with FastAPI and Backend applications • Familiarity with data platforms like Databricks or Snowflake • Exposure to SRE practices or cloud security certifications • Hands-on experience with Prometheus, Grafana, or Datadog Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys • Relocation support for employees moving to join the team in one of our office locations • A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

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 This role is for Nebius AI R&D, a team focused on applied research in AI. Examples of applied research that we have recently published include: • applying reinforcement learning for agent training in long-context multi-turn scenarios • dramatically scaling task data collection to power reinforcement learning for SWE agents • building a decontaminated evaluation for SWE agents that is regularly updated • investigating how test-time guided search can be used to build more powerful agents The results often lead to collaboration with adjacent teams where our research findings are applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: • Guided search and reinforcement learning for agentic systems • Reinforcement learning for reasoning models • Web-scale problem collection for training agents • Efficient model distillation Some examples of what your responsibilities might include are: • Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments • Exploring methods of guided generation and search in the trajectory space • Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training • Conducting experiments with different reinforcement learning configurations in verifiable domains • Exploring methods to train AI agents on tasks with non-verifiable reward signals We expect you to have: • A profound understanding of theoretical foundations of machine learning and reinforcement learning • Deep expertise in modern deep learning for language processing and generation • Substantial experience with training large models on multiple computational nodes • Strong software engineering skills (we mostly use python) • Deep experience with modern deep learning frameworks (we use jax) • Strong communication and leadership abilities • Experience designing, executing, and analyzing machine learning experiments with proper statistical rigor • Ability to formulate research questions, design experiments to test hypotheses, and draw meaningful conclusions from results • Ability to document research findings clearly and contribute to technical publications or report Nice to have: • Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, PPO etc • Familiarity with important ideas in LLM space, such as RoPE, ZeRO/FSDP, Flash Attention, quantization • Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Master’s or PhD preferred • Track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment • Experience in engineering complex systems, such as large distributed data processing systems or high-load web services • Open-source projects that showcase your engineering prowess • Excellent command of the English language, alongside superior writing, articulation, and communication skills • Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing 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.

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 As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities • Own optimization projects for specific model families, customer endpoints, or serving backends. • Compare inference engines and recommend serving configurations suited to individual workloads. • Diagnose model-quality and performance regressions during production rollouts. • Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. • Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. • Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. • Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. • Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. • Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. • Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. • Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. • Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves • Strong engineering skills in Python and PyTorch. • Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. • Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. • A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. • Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. • Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. • Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves • Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. • Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. • Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. • Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. • Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. • Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. • Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. 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.