Machine Learning Engineer - Defense

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.

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.

At EpiSci, you will:

• 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

We're looking for someone who has:

• 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., GitHub 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

Travel Requirements:

• Must be willing to travel as projects require, usually for SW/HW integration and/or demonstrations; estimated average travel is 2-5 days every other month (10-20%)
• Interested candidates should also inquire about relocation assistance to Applied Intuition Defense facilities near Destin, FL, Sunnyvale, CA, or Ann Arbor, MI

Security Requirements:

• Must be a U.S. Citizen
• Must hold or be eligible for a U.S. Secret security clearance

Nice to have:

• PhD in Computer Vision, Machine Learning, or related field with strong academic contributions
• 5+ years of experience building and deploying perception models on real-world hardware
• A background in generating datasets using simulation engines like Unreal Engine, NVIDIA Omniverse, or Blender
• Experience with deeply optimizing Transformer-based models for edge devices like NVIDIA Jetson boards
• Proficiency in modern C++ (2020, 2023), CMake, Conan
• Familiarity with Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL), including integration and test experience on common COTS hardware devices (e.g., NVIDIA Jetson, Raspberry Pi5, M.2 accelerators)
• Willness to relocate to facilities near Destin, FL, Washington, D.C., or San Diego, CA

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 subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $150,000 to $225,000 USD annually.