Software Engineer - E2E Autonomy
Applied IntuitionSunnyvale, California, United StatesPosted 26 March 2026
Job Description
About Applied Intuition
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .
We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.
About the role
Applied Intuition is pushing the frontier of autonomy and vehicle intelligence through next-generation AI Research. In this role, you will enable this research effort by building ML tools, infrastructure, managing large datasets, and addressing challenges across the entire end-to-end (E2E) autonomy stack. Join our research and production teams in creating the next generation of self-driving vehicles. Here at Applied, we encourage all engineers to take ownership over technical and product decisions, closely interact with external and internal users to collect feedback, and contribute to a thoughtful, dynamic team culture.
At Applied Intuition, you will:
Build tools and infrastructure to enable large-scale E2E autonomy research led by Dr. Wei Zhan
Work directly with both AI research and engineering teams to productionize next generation self-driving software
Help scale end-to-end training by identifying bottlenecks, especially as we continue to scale our GPU compute, onroad data volume, curation efforts, and eval systems
Technologies: Python, Pytorch, CUDA, Bazel, Kubernetes, Spark, Flyte
We're looking for someone who has:
Exceptional software generalist experience with ambition to contribute up and down the ML stack, onboard the vehicle, and in the cloud
Strong fundamental skills in software module design: can tame chaos with libraries abstractions
Rapid analytical and problem-solving skills
Nice to have:
Strong multi-language build system knowledge
Distributed systems/compute (e.g. k8s, cloud-managed services at scale)
ML infra fluency - has used and/or contributed to ML and large data systems
Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.
Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.
Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, ... (truncated, view full listing at source)
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