Top Tech Jobs & Startup Jobs

Operate and enhance security operations capabilities by developing SIEM detections, integrating security tools, analyzing telemetry, triaging alerts, investigating incidents, and building SOAR playbooks. The role validates logging and data coverage, collaborates with infrastructure and engineering teams on containment and remediation, and maintains incident-response documentation. It requires 1–2 years of hands-on security operations experience and practical SIEM administration, with scripting, automation, and SOAR experience preferred.

Develop and maintain C++ embedded applications running on Ubuntu across custom chips and x86 platforms. Design secure distributed software for multi-chip environments, participate in requirements through testing within an ASPICE-aligned process, use AI-assisted development tools, write automated tests, and support continuous integration. The role requires knowledge of secure software development, distributed applications, networking, automotive protocols, and GoogleTest.

Drafts, reviews, and negotiates complex global commercial agreements with customers, vendors, partners, and service providers. Provides practical advice on commercial, regulatory, privacy, licensing, distribution, procurement, and intellectual property matters. Develops legal and compliance processes, leads cross-functional projects, and partners with global business stakeholders. Requires approximately eight years of relevant legal experience, strong negotiation skills, business judgment, and fluency in English and Hebrew.

Research and develop image and video generative models for autonomous driving, focusing on temporal consistency, multiview coherence, controllable scene generation, and world models. Own model experimentation, training, evaluation, and workflow integration. Build scalable Python and PyTorch implementations, collaborate with AI and perception teams, and help shape research direction. Work may include diffusion and flow-based models, foundation models, multimodal systems, distributed training, and large-scale proprietary driving datasets.

Develop and optimize computer vision, deep learning, and geometric modeling algorithms for autonomous-driving lane detection and data-generation workflows. Build scalable internal tools and pipelines, collaborate with development and annotation teams, and evaluate algorithm performance, robustness, and efficiency from prototyping through deployment.

Lead Mobileye’s Release Management team and oversee end-to-end ADAS and autonomous vehicle software releases. Responsibilities include defining release processes, coordinating cross-functional planning, managing governance and readiness reviews, tracking risks and KPIs, resolving blockers, improving automation and delivery pipelines, and developing release management professionals.

Lead product strategy and roadmap for cloud and multi-petabyte data services and AI-native agent products. Own requirements, stakeholder engagement with internal teams and OEMs, cross-functional execution, hands-on validation, release planning, adoption tracking, and product documentation to drive scalable, self-service tooling for engineers.

Build and optimize distributed data, evaluation, and ML pipelines for 3D perception models in autonomous driving. Develop tools for image, video, geometric, and model-output processing; improve memory efficiency, throughput, and computation costs. Own model packaging, distribution, and deployment interfaces, while coordinating data contracts and workflows across research, infrastructure, data, and internal customer teams.

Develop and optimize transformer-based temporal vision models for 3D perception in autonomous vehicles. Analyze errors, improve driving-scene understanding, research advances in 3D detection and perception, and work with large-scale driving datasets. The role requires Python development, rigorous experimentation, mathematical and analytical expertise, and collaboration within a large research-oriented team.

Defines system-level hardware architectures for EyeQ-based automotive compute platforms. Integrates SoCs with MCUs, memories, cameras, radars, lidars, SerDes, and networking components while driving trade-offs involving performance, power, thermal constraints, cost, safety, security, and scalability. Translates customer and product needs into requirements and interface specifications, influences SoC and platform architecture, and collaborates with engineering, product, OEM, supplier, and technology partner teams through design, bring-up, and validation.