Sensor Sim - ML Engineer

Applied Intuition
Sunnyvale, California, United StatesPosted 28 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. Meet our software engineers! Meet some of our software engineers who are shaping the future of autonomy and delivering world-class solutions helping customers shorten time to market. Hear about what brought them to Applied Intuition, what’s kept them interested, and their advice to potential candidates. About the role We are looking for a software engineer to join our team working on the incorporation of modern machine learning approaches into production-grade sensor simulation. In this role you will work with our research team to apply state of the art approaches to model worlds and sensors such as Lidars, Radars and Cameras. At Applied Intuition, you will: Drive research, development and deployment of generative and other machine learning techniques into our sensor simulator. Work closely with rendering and physics-based modeling teams to build novel approaches fusing the best of all techniques. Focus on projects with a clear path to production value and customer impact . We're looking for someone who has: A Bachelor's degree in Computer Science, Software Engineering, or equivalent 5+ years of experience building software components or (sub) systems that address real-world machine learning challenges A passion for turning their domain expertise into tooling that boosts the productivity of teams working on various real-world applications of autonomous systems Hands-on experience with evaluation and optimization for large generative models Hands-on experience with agentic system design Deep understanding of machine learning foundations who can apply various techniques to new problems Understanding of 3D geometry, optical flow or video generation Nice to have: Experience working with Generative World Models (Cosmos, UniSim, sora-style architectures) Experience with robotics simulation products (IssacSim, MuJoCo, Omniverse, USD) Experience with applying synthetic data to machine learning tasks Hands on experience with characterization of models for Lidar, Radar, and Camera 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 sa ... (truncated, view full listing at source)
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