Machine Learning Engineer - Defense

Applied Intuition is the vehicle intelligence company that accelerates the global adoption of safe, AI-driven machines. Founded in 2017 and now valued at $15 billion following its recent Series F funding round, Applied Intuition delivers the Vehicle OS, Self-Driving System, and toolchain to help customers build intelligent vehicles and shorten time to market. 18 of the top 20 global automakers and major programs across the Department of Defense trust Applied Intuition's solutions to deliver vehicle intelligence.

Applied Intuition services the automotive, defense, trucking, construction, mining, and agriculture industries and is headquartered in Mountain View, CA, with offices in Washington, D.C., San Diego, CA, Ft. Walton Beach, FL, Ann Arbor, MI, London, Stuttgart, Munich, Stockholm, Bangalore, Seoul, and Tokyo. Learn more at

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.

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Note:

For Epi Sci job openings, fully remote work will be considered by exception.)

Epi Sci, an Applied Intuition company, is redefining tactical mission autonomy. These technologies span across defense and commercial domains. At our core, we wield the latest advancements in artificial intelligence, autonomy algorithms, wireless communications, and digital signal processing to surmount the most formidable national security challenges. Our tactical AI offers robustness, dependability, and a rapid adaptability that thrives on the front lines of emerging missions and obstacles.

Our AI increases human capability in aircraft like the F-22 and F-16; it’s revolutionizing wireless tactical communication systems and filling the skies with swarms of autonomous UAVs supercharged by sensor fusion.

As a Machine Learning Engineer at Applied Intuition Defense, you will be pivotal in developing, integrating, and maintaining real-time AI/ML solutions deployed across a range of heterogenous autonomous vehicles and different domains (e.g., land, air, sea, and space). You will work with a team to continuously add capability and demonstrate the solution to customers in real-world scenarios on a variety of hardware platforms.

You will be responsible for rapidly designing, developing, and integrating AI/ML models to interface across different platforms, processing data in real-time with real military operators in the loop. Your work will span the MLOps pipeline, improving ingestion and tooling, labeling and autolabeling, model architectures, training, evaluation and validation, inference-time optimization, and inference service deployment. You will have access to large training clusters with the latest GPUs, and the best AI talent and knowledge in the industry through Applied Intuition commercial ML.

• Develop, integrate, and adapt cutting-edge AI/ML algorithms running on the perception autonomy stack to process aerial imagery across a variety of platforms and sensor types (e.g. EO, IR).
• Work with the best and most competitive AI talent in the world through collaboration with Applied Intuition commercial product staff
• Scale up datasets using a variety of state-of-the-art data generation techniques including simulation, diffusion, and gaussian splats
• Create inference software providing low-latency, real-time feedback to autonomy software on-board live platforms
• Collaborate across the hardware, sensor, tracking, autonomy, and testing teams to ensure seamless deployment in on-site DoD testing and demonstration events
• Leverage software-in-the-loop and hardware-in-the-loop testing and profiling to collect performance data
• Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience

• MS or PhD in Computer Engineering, Robotic Engineering, Computer Science, or equivalent OR 5+ years of relevant experience working with simulation, machine learning, and ML infrastructure. Proficiency in training ML models in PyTorch on multi-machine, multi-GPU systems.
• Experience in optimizing and deploying machine learning models to edge devices
• Strong Python knowledge and high capability in C++
• A core understanding of sensor physics and sensor parameters
• Experience leveraging modern AI-powered development tools (e.g., Git Hub Copilot, Cursor) to accelerate the creation of robust, well-tested systems
• Adeptness with remote software development, the ability to handle and process large datasets, and a capacity to learn new software and algorithms as needed with little supervision

• Must be willing to travel as projects require,…