Top Machine Learning Jobs in Israel

Leads end-to-end R&D programs for data platforms, AI applications, and CAD-based solutions, from definition and development through release, customer deployment, and installed-base support. Coordinates multidisciplinary teams across software, computing, operations, field service, product, and procurement. Owns program scope, schedules, budgets, risks, phase gates, governance, releases, customer readiness, upgrades, and senior-management reporting.

Lead corporate strategy for Arpeely and its Ace AI browser. Responsibilities include defining company positioning, developing strategic partnerships, analyzing competitors and market trends, shaping product and go-to-market strategy, evaluating acquisitions and investments, building financial models and theses, and managing annual and quarterly planning. The role also supports strategy sessions, financial performance tracking, and company communications.

Develop algorithms and full-stack applications for validating neural networks and autonomous driving systems. Design Ground Truth generation solutions, solve mapping and geometric analysis problems, evaluate algorithm performance statistically, and build scalable tools for visualization and data analysis. Research advanced techniques, collaborate across disciplines, and own projects from problem definition through implementation, integration, and performance evaluation.

Build end-to-end validation processes, infrastructure, tools, and methods for Mobileye’s mapping and localization platform. Develop geometric and heuristic validation tools, improve pipeline performance, integrate algorithms from R&D teams, use AI coding tools, and analyze large-scale cloud data to produce actionable conclusions.

Design, implement, debug, and optimize embedded Linux software for semiconductor equipment and devices. Integrate hardware-aware software with high-level systems using protocols such as gRPC, make architecture and performance decisions, collaborate with hardware, FPGA, software, and algorithm teams, and develop scalable solutions across Intel and ARM architectures.

Own the end-to-end lifecycle of AI OS components, from ideation and design through launch and continuous iteration. Partner with engineering, forward-deployed teams, and enterprise customers to improve agent reliability and deployment success. Define product metrics, analyze customer usage, prioritize opportunities, and adjust strategy to maximize impact and growth in a fast-paced, ambiguous environment.

Design and develop Java backend software for scan-domain system flows in semiconductor metrology and inspection products. Build core logic for scanning, sequencing, and execution; create scalable, reliable backend components; collaborate with algorithm, hardware, and systems teams; participate in architecture and technical decisions; and troubleshoot, debug, and optimize complex system-level issues.

Develop and optimize machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, developing RAG and fine-tuning workflows, evaluating model performance, and integrating production systems with engineering and research teams.

Conduct advanced security research focused on AI models, agentic platforms, and infrastructure. Investigate vulnerabilities, prompt injection, adversarial manipulation, and model abuse; build proof-of-concepts; perform red-team testing; track competitors and industry trends; and collaborate with engineering, product, marketing, and customer teams to turn research into practical AI security protections and thought leadership.

Develop and train deep learning models for autonomous-driving perception, including object detection, scene understanding, and 3D perception. Design and analyze end-to-end pipelines from data preparation through deployment, collaborate with algorithm, software, and data teams, and research methods in computer vision, multi-task learning, and robotics to improve production model performance and efficiency.

Lead hands-on AI/ML research across retrieval, agents, model routing, multimodality, and evaluation infrastructure. Shape the research agenda, define measurable quality standards, manage a small research team, and partner with product and engineering to ship research-driven features. The role requires continued coding and experimentation, production AI/ML experience, and a track record of turning research into products.

Leads a multidisciplinary team across systems engineering, optics, and embedded software. Owns system architecture, integration, calibration, validation, performance optimization, customer issue resolution, and engineering process improvement for vehicle inspection systems. Collaborates across optics, mechanics, electronics, software, and algorithms while driving technical decisions and product development in a fast-paced startup environment.

Develop and validate computer vision and image-processing algorithms for advanced imaging systems. Optimize CPU and GPU performance, memory usage, and data movement while maintaining accuracy and robustness. Design experiments using real-world imaging data, build tested algorithm components, and collaborate with software, physics, optics, hardware, and applications teams on integration and deployment. Document findings, explain tradeoffs, and use AI-assisted development tools responsibly.

Research and develop agentic AI and NLP systems for sovereign applications, including orchestration, planning, tool use, retrieval, information extraction, enrichment, benchmarking, simulation, and evaluation. Own projects from problem definition through deployment, collaborate with engineering, assess system failure modes, improve models through experimentation and feedback, and mentor researchers while operating under security, compliance, compute, and deployment constraints.

Develop production algorithmic systems that transform inconsistent documents, drawings, imagery, and other data into reliable geospatial intelligence. Responsibilities include problem definition, data collection and processing, relevance and reliability assessment, document and image understanding, coordinate anchoring, evaluation methodology, and production deployment. The role applies computer vision, geometry, classical algorithms, machine learning, and LLM-based approaches while collaborating with mapping experts, surveyors, and researchers.

Own end-to-end data science projects for a mobile gaming company, including game economy optimization, reward and personalization modeling, simulations, experimentation, causal inference, and player behavior analysis. Build AI-powered tools, develop experimentation capabilities, and translate findings into product and business recommendations while collaborating with Product, Development, Game Economy, and BI teams.

Investigate and respond to connected-vehicle and API security incidents, lead complex investigations, conduct threat hunting, tune detections, and improve SOC playbooks. Deploy and maintain agentic AI workflows for triage and response, monitor their performance, advise customers, mentor analysts, produce reports, and support rotating on-call operations.

Lead Alice Labs’ AI safety and security research agenda, covering model evaluation, red teaming, adversarial robustness, LLMs, agents, and multimodal systems. Own the research roadmap, design industry-leading benchmarks, publish at top-tier conferences, mentor senior researchers, manage academic and industry partnerships, and translate research into products. The role also represents Alice externally at conferences, partner organizations, and frontier labs, requiring significant international travel.

Annotate and validate vehicle inspection images according to detailed guidelines, producing accurate labeled data for machine learning and computer vision models. The role requires sustained concentration, consistency, attention to detail, strong English communication, technical computer skills, and the ability to work independently and collaboratively.

Own Optibus’s Rail product from zero to one, including discovery, MVP definition, design-partner validation, first customer deployments, roadmap, OKRs, and scalable product strategy. Collaborate with engineering, design, sales, marketing, customer success, and onboarding teams to establish product-market fit, shape go-to-market plans, and deliver successful rail and multimodal transit solutions.

Define and drive the AI product vision, strategy, and roadmap for semiconductor metrology solutions. Translate customer needs, industry trends, and emerging AI technologies into product opportunities, requirements, and priorities. Lead initiatives from concept through development, validation, production, and customer adoption while collaborating with R&D, software, engineering, applications, field teams, and customers. Evaluate technical trade-offs, monitor competitive developments, and influence cross-functional stakeholders to deliver scalable, production-ready products.

Drive end-to-end execution across engineering, research, validation, data, and infrastructure teams. Build execution plans, track KPIs, resolve blockers, manage risks, align roadmaps with compute and data constraints, and facilitate reviews using dashboards and performance reports. The role requires hands-on technical understanding of code, scripts, CI/CD, testing, data and machine learning tools, and deep learning development processes.

Lead the research, design, development, deployment, and optimization of scalable machine learning systems. Build online models using deep learning, NLP, LLMs, and recommendation systems; translate business questions into experiments; work with massive datasets and production workflows; and collaborate with data scientists, engineers, product managers, analysts, and developers. The role also involves leading machine learning projects and applying modern AI development and MLOps practices.

Lead the architecture and implementation of an enterprise data warehouse in AWS. Design scalable data platforms, pipelines, data lakes, and analytical structures using AWS services, Python, Spark, Airflow, Parquet, and Iceberg. Provide technical leadership to data engineers, define technology roadmaps, and collaborate with development, data science, and business teams to support BI, AI, and generative AI initiatives.

Leads the Performance Management engineering organization, overseeing software design, development, testing, deployment, scalability, capacity planning, and operational support. Owns resource planning, delivery milestones, engineering standards, product quality, and integration of customer requirements. Leads a globally distributed team, recruits and develops engineering talent, and advances adoption of AI, LLM, agentic technologies, microservices, containers, and cloud platforms.