Senior Controls Engineer (Federal)

FieldAI•Atlanta, GA

1d•Onsite

About The Position

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. At FieldAI, we build autonomous robotic systems that operate in demanding, real-world environments where tight integration between hardware and software is critical. We’re looking for a Senior Robotics Engineer to lead the design, implementation, and deployment of advanced control systems and collaborative behaviors for our autonomous vehicle fleet. In this role, you will serve as a key planning and controls expert, solving complex challenges involving operating in unstructured environments. You will own the evaluation and implementation of advanced control schemes, ensuring computationally constrained systems operate reliably across new and existing robotic platforms. Furthermore, you will drive the development of the controller level safety layer for safe robot operations. This is a highly impactful role for an engineer with deep expertise in control theory and robot safety who can translate advanced research into robust, field-ready solutions.

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

Nice To Haves

Experience with field robotics, off-road vehicles, or high-speed / safety-critical robotic systems.
Background in control theory, including classical and learning-based controllers.
Experience with low SWaP (Size, Weight, and Power) robotic platforms.
Prior ownership of system integration or technical leadership for a robotic platform.
Experience mentoring junior engineers or leading integration efforts in small teams.
Familiarity with autonomy stacks, planning systems, or real-time robotics software architectures.
Knowledge of containerization (Kubernetes) and modern DevOps practices.

Responsibilities

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.
Execute, test, and deploy multi-agent behaviors to ensure the timely delivery of capabilities for key customer milestones.
Deploy theoretical control schemes and multi-agent behaviors onto real-world robots operating in demanding, dynamic environments.
Leverage deep domain expertise in off-road field robotics to ensure software robustness against unpredictable terrain and environmental conditions.
Bridge the gap between advanced multi-agent research and practical, reliable product development for real-world deployments.
Serve as a resident planning and controls expert, filling critical knowledge gaps and guiding technical strategy for the engineering team.
Lead the effort to generalize highly specific robot controllers, ensuring new robots can be brought online quickly and efficiently.
Ensure the timely delivery of critical autonomy capabilities to fulfill broader company goals, operational promises, and customer contracts.