Senior Controls Engineer – Federal

🕒 April 27

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Field AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

Artificial Intelligence • Robotics • Construction

Field AI is a technology company focused on pioneering robotic AI and autonomy. The company develops Field Foundation Models (FFMs) that enable robots to operate autonomously in unpredictable and complex environments without the reliance on GPS or pre-defined paths. With its advanced AI solutions, Field AI is transforming various industries by enhancing operational efficiency, safety, and precision in real-world applications, particularly in sectors such as construction, oil and gas, and federal safety.

📋 Description

• Identify and model the dynamic behavior of autonomous vehicles to establish accurate baselines for motion planning. • Adapt and train control models to transition between diverse vehicle platforms. • Develop, optimize, and implement robust control stacks across the fleet to ensure safe and reliable operations. • Evaluate advanced control schemes for varying vehicle architectures. • Design and implement computationally efficient control and planning algorithms tailored to the hardware and compute limitations. • Verify and validate new control schemes directly on physical robots, overcoming bottlenecks when porting controllers to new platforms. • Lead architectural design discussions for controller level safety layer. • Develop and implement robust low-latency safety behaviors for our vehicle fleets operating in unstructured environments. • Deploy theoretical control schemes and multi-agent behaviors onto real-world robots operating in demanding, dynamic environments. • Serve as a resident planning and controls expert, filling critical knowledge gaps and guiding technical strategy for the engineering team.

🎯 Requirements

• Ph.D. in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on planning, controls, or multi-agent systems; OR an MS degree in a related field with 3+ years of relevant industry experience. • Deep expertise in modern control theory, specifically with predictive and sampling-based control methodologies (such as MPC and MPPI), and vehicle dynamics modeling. • Proven experience deploying complex control and planning algorithms onto physical robots, rather than just in simulation. • Experience working with large-scale or off-road autonomous vehicles operating in unstructured, real-world environments. • Strong ability to design and implement computationally constrained control schemes tailored to the compute limitations of embedded robotic platforms. • Ability to verify, validate, and debug complex control architectures, overcoming system-level bottlenecks when adapting software to new vehicle platforms. • Strong ownership mindset with the ability to serve as a resident technical expert, bridging the gap between advanced autonomy research and reliable product deployment. • Hands-on experience with C++, Python, CUDA. ROS1/2, Docker, Linux

🏖️ Benefits

• full benefits • equity • generous time off