Navigation and Controls Engineer

About Us At Intramotev, we are dedicated to revolutionizing the freight industry by liberating tons of freight from congested highways, reducing carbon emissions, and enhancing transportation efficiency. We are deploying self-propelled battery-electric railcars and vehicles to transform freight logistics and bring innovation to our rail network. Based in St. Louis, MO, we are committed to promoting industrial revitalization, saving lives, and minimizing the environmental impact of trucking and transportation. We actively foster a work environment for every teammate that's welcoming, respectful and inclusive, with great opportunities for professional growth. Find your future with us. Role Overview We are deploying self-propelled battery-electric railcars and vehicles, and we're hiring a Navigation and Controls Engineer to own the software that gets them there safely: the code that turns a dispatched trip into a trajectory, the control laws that carry it out, and the logic that figures out where the vehicle actually is at any given moment. Rail removes the steering axis, which makes this a fundamentally 1-D constrained problem — longitudinal control, speed profiling, and stopping accuracy against several tons of moving railcar are the whole game rather than one axis of three. You'll carry the algorithm from model through implementation through field validation, working against trackfiles, consists, and the site-server messages that drive dispatch. This is a full-time, 100% in-office role based in St. Louis, MO. You'll work within a team of 3–10 engineers and engage in occasional domestic travel for testing, integration, demonstrations, and customer interactions. Key Responsibilities Motion Planning & Trajectory Generation: Write the software that turns a dispatched trip into a trajectory — from global route to local path, trajectory optimization, time parameterization, and replanning against constraints that change mid-trip. Control Law Design: Develop and tune control laws in MATLAB/Simulink using classical and modern control theory — state-space methods, stability and margin analysis, and closed-loop tuning against a plant model — built to run on an embedded real-time target, not only in simulation. State Estimation & Sensor Fusion: Build Kalman-filter-based (KF/EKF/UKF) state estimation that fuses GNSS/INS and vehicle sensor data into a position, speed, and heading estimate a control law can safely act on, including degraded-GNSS behavior. Vehicle Dynamics & Safety-Critical Design: Bound what a controller can command against mass, grade, adhesion, braking, and propulsion limits, and treat failure modes, degraded modes, and fail-safe behavior as part of the algorithm itself. Validation & Field Testing: Design test cases in simulation, analyze field-test data, and close the loop between what the model predicted and what the vehicle did. Collaboration: Partner with the Perception team on sensor and localization inputs, with Embedded on the real-time targets the algorithms run on, and with the Controls Simulation effort on the environment used to validate changes before they reach a vehicle. Compensation & Benefits Base salary: $90,000 - $110,000 Intramotev offers a comprehensive benefits package for all team members, including: Medical coverage: We cover 100% of employee medical premiums, plus low-cost dental and vision options. Vacation: Full-time employees enjoy unlimited paid time off. Equity: Full-time employees receive equity in the company. Parental leave: New parents receive paid time off to spend quality time with their family. About You: Basic Qualifications (Required Skills/Experience) 3 or more years of professional experience developing guidance, navigation, or control systems for vehicles, robots, or comparable dynamic platforms — shipped systems, not coursework alone. Classical and modern control theory in practice — state-space methods, stability and margin analysis, closed-loop tuning against a plant model, and the judgment to know when a simpler controller is the right answer. Model-based design in MATLAB/Simulink, including the discipline of building models intended to run on an embedded real-time target rather than only in simulation. State estimation and sensor fusion — Kalman filtering (KF/EKF/UKF) or equivalent, fusing GNSS/INS and vehicle sensor data into a position, speed, and heading estimate a control law can safely act on. Motion planning and trajectory generation — global route to local trajectory, trajectory optimization, time parameterization, and replanning against constraints that change during a trip. Working knowledge of vehicle dynamics — how mass, grade, adhesion, braking, and propulsion limits bound what a controller can actually command. Software proficiency in C, C++, or Python sufficient to implement, integrate, and debug an algorithm in the production codebase — not only to hand a model across a wall. Grounding in safety-critical design — failure modes, degraded modes, and fail-safe behavior treated as part of the algorithm, not as a downstream review step — paired with validation discipline: designing test cases in simulation, analyzing field-test data, and closing the loop between what the model predicted and what the vehicle did. Git in a collaborative workflow (branching, merging, pull requests, code review) and comfort working in a Linux command-line environment. Fluent, demonstrated use of AI-assisted development tools (Claude, Cursor, Copilot, etc.) on real work, and a thoughtful point of view on when they help and when they get in the way. Fluent in both written and verbal English. U.S. Person (immigration or work visa sponsorship will not be provided). Preferred Qualifications (Desired Skills/Experience) Candidate should have a working knowledge of one or (ideally) multiple areas listed below: Master's or PhD in controls, robotics, aerospace, mechanical, or electrical engineering. Model Predictive Control on a real system, including how the horizon and cost weights were actually chosen. Search-based or sampling-based planners (A*, hybrid A*, RRT-family) and where each breaks down. Optimal control — LQR/LQG, direct or indirect trajectory optimization. Embedded code generation from Simulink (Embedded Coder) and the practical constraints of the generated code on a real-time target. Hardware-in-the-loop or software-in-the-loop test infrastructure exposure — a software-in-the-loop framework is being built out now; the resulting environment is where this role's algorithms get validated. Rail domain exposure — train dynamics, consist behavior, slack action, braking curves, adhesion limits, signaling or PTC-adjacent systems. Autonomous or unmanned vehicle experience (automotive AV, off-highway, marine, aerial, defense). Perception and localization exposure — lidar, radar, camera, or map-relative localization — enough to design a controller around what perception can and cannot deliver. Experience taking an algorithm from prototype through field deployment and customer handover. ROS or a comparable robotics middleware. Reading and extending an existing controls codebase, not only greenfield work. Typical Education and Experience Education/experience typically acquired through advanced technical education (e.g. Bachelor and/or Master) and typically 3 or more years' related work experience, or an equivalent combination of technical education and experience. Relocation Intramotev offers relocation based on candidate eligibility. Equal Opportunity Employer Intramotev is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law. Our Interview Process We evaluate depth of understanding over breadth of buzzwords. Our process includes a brief technical screen focused on fundamentals, a short technical presentation from you (details below), and a final on-site conversation with the engineering team. We provide clear expectations before each stage and aim to complete the process within three weeks. For the presentation, we ask for a short 5 to 10 minute talk on a technical topic you've worked on in the past in your area of expertise. Something you can go in depth on for at least 5 minutes. Ideally, this is a very narrow part of a big project, but go as deep as you can in those 5 to 10 minutes. Closing If you're passionate about navigation and controls and eager to contribute to autonomous rail technology, we'd love to hear from you!