Mobileye is looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables Mobileye’s algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow Mobileye to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.
As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.
What will your job look like?
- Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for Mobileye developers
- Expand the capabilities of existing platforms by embedding smart, context-aware LLM features
- Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them
- Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG)
- Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves
All you need is:
- B.Sc. in Computer Science, Software Engineering, or a related technical field
- 4+ years of hands-on software engineering experience, with a strong focus on Python development
- Proven experience building and deploying production LLM applications, including agentic workflows, tool calling, and context management
Nice to Have:
- Deep understanding of CI/CD pipelines, DevOps practices, and automated build/test systems
- Experience with cloud infrastructure and modern deployment architectures
- Familiarity with Git and GitLab, backed by practical experience in automating developer workflows
Skills Required
- B.Sc. in Computer Science, Software Engineering, or related technical field
- 4+ years of hands-on software engineering experience
- Strong focus on Python development
- Proven experience building and deploying production LLM applications, including agentic workflows, tool calling, and context management
- Deep understanding of CI/CD pipelines, DevOps practices, and automated build/test systems
- Experience with cloud infrastructure and modern deployment architectures
- Familiarity with Git and GitLab and automating developer workflows
What We Do
Mobileye is leading the mobility revolution with its autonomous-driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, machine learning, mapping, and data analysis. Founded in 1999, Mobileye has pioneered such groundbreaking technologies as REM™ crowdsourced mapping, True Redundancy™ sensing, and the RSS™ safety model. These technologies are driving the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions, powering industry-leading advanced driver-assistance systems and delivering valuable intelligence to optimize mobility infrastructure. Mobileye technology is used in over 170 million vehicles worldwide. In 2022, Mobileye became an independent company while still being majority-owned by Intel. Mobileye’s headquarters and R&D center are based in Jerusalem, with additional offices across Israel and around the world.
Why Work With Us
Our technology enables self-driving vehicles and mobility solutions, powers industry-leading advanced driver assistance systems, and delivers valuable intelligence to optimize mobility infrastructure.
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