Top Machine Learning Jobs in Israel

As a CBRN expert at Alice.io, you will evaluate and create prompts for Generative AI systems, ensuring their safety and accuracy in CBRN contexts. You'll provide expert feedback and stay updated with relevant research and regulations.

Lead technical projects in the System Product Engineering group by managing project lifecycles, collaborating with multiple teams, and improving product quality through advanced testing and analysis.

Senior technical leader designing, developing, and deploying production-grade AI for medical imaging. Own end-to-end model lifecycle (data ingestion, training, evaluation, deployment, monitoring). Focus on vision-language, diffusion, and multimodal models; collaborate with scientists, engineers, product, and clinical experts to productionize AI solutions.

Lead research initiatives to enhance AI products, drive innovation, and collaborate across teams to meet customer needs in AI solutions.

As a Machine Learning Engineer, you will design and deploy ML models, collaborate with cross-functional teams, monitor performance, and utilize MLOps best practices.

Design and deploy advanced ML algorithms using on-chip telemetry for lifetime prediction, clustering, parametric modeling, and time-series forecasting. Lead end-to-end algorithm lifecycle from research and POC to validation and deployment, collaborate with cross-functional teams and customers, and integrate AI-enhanced tools and intelligent pipelines into production ML workflows.

The role involves developing AI-enhanced methodologies for pre-silicon functional verification, engaging with customers, and collaborating with engineering teams.

The role involves applying machine learning to optimize verification methodologies, specifically for ASIC pre-silicon verification, collaborating with teams, and engaging with customers on innovative strategies.

The Senior AI ML Solution Engineer designs AI development pipelines, evaluates ML methods for code generation, and ensures quality in AI-generated code. Responsibilities include creating evaluation systems, leading proofs of concept, and collaborating on risk-based development strategies.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Lead car insurance pricing strategy by identifying opportunities, building and improving pricing and risk models using actuarial methods, ML and AI tools, partnering with Data Science, Product, Engineering and Finance, defining metrics (loss ratio, rate adequacy, conversion, retention), and translating complex pricing problems into scalable product and platform requirements.

Lead foundry-facing engineering to drive yield and performance across advanced nodes. Analyze large IC parametric datasets, develop data pipelines, build automated monitoring/alerting, and coordinate cross-functional teams and foundry partners to identify and contain process excursions.

The Machine Learning Engineer will develop AI driver models, manage training algorithms, and conduct on-road evaluations to enhance automated driving safety and performance.

As a Senior AI&ML Researcher, you will develop advanced machine learning and agentic AI systems for Workforce Management, integrating solutions and optimizing scheduling in production environments.

The Senior Software Engineer will design and develop infrastructure for AI algorithms in medical imaging, collaborate with teams, and maintain code quality.

Lead cross-domain engineering initiatives connecting AI algorithms, software, and data systems. Ensure AI architecture reliability and efficiency while mentoring engineers and driving collaboration.

Lead the design and development of AI-driven systems, influencing technical decisions, mentorship, and collaboration across teams to enhance clinical AI solutions at Aidoc.

The AI/ML Engineer will design and implement LLM-powered systems, integrate APIs, optimize RAG pipelines, and work collaboratively to extract insights from data.

Lead design, build, deploy, and operate production AI systems across classical ML, computer vision, LLMs, and agentic workflows. Create scalable model-serving and workflow architectures, evaluation strategies, observability and feedback loops, optimize inference and cost, and provide technical leadership, code reviews, and mentoring.

Design and improve ML-driven data curation pipelines for large autonomous-vehicle datasets: embedding extraction, VLM scene analysis, scoring, deduplication, sampling, GPU inference at scale, validation/diagnostics, dataset SDK and 3D geometry tooling, and collaborate with algorithm and infra teams to close data gaps.

Build and improve ML-driven data curation pipelines for autonomous-vehicle datasets: run GPU inference at scale, extract embeddings, score/deduplicate/sample data, ensure rare scenarios are represented, validate dataset quality, and contribute to dataset SDKs and 3D geometry tooling while collaborating with algorithm and infra teams.

Lead strategy, roadmap, and delivery of ML products for global supply chain. Partner with data science, engineering, and business to take models from concept to production with strong MLOps, data readiness, governance, and measurable business impact. Build practice, mentor teams, and operationalize Responsible AI and value tracking.

Design, build, and deploy ML systems (RL, Bayesian inference, probabilistic models) for calibration, control, and autonomous operation of quantum processors. Implement real-time steering, agentic control frameworks, Python ML services integrated with Quantum Machines stack, and collaborate with customers, R&D, and hardware teams to validate solutions on real experimental hardware.

Develop, train, debug, and improve computer vision and 3D perception models for ADAS. Own end-to-end ML lifecycle work (data, training, evaluation, iteration), build scalable data pipelines including auto-/pseudo-labeling, and contribute to online (in-car) and offline perception systems for detection, segmentation, tracking and 3D reconstruction.

Design and build production-grade autonomous agentic systems for supply chain problems. Architect multi-agent workflows, integrate ML/ deep learning models, evaluate agent performance, and collaborate with engineering and product teams to productize, monitor, and iterate agent pipelines.