Job#: 3054137
Job Description:
Software Engineer Simulation Vehicle Modeling
Location: Ann Arbor, Michigan (Remote)
Full Time Employment Opportunity (cannot provide visa sponsorship now or in the future)
Company Overview & Culture
At our organization, the simulation teams are building the foundation for AV 3.0 driver-out development and certification. This team serves as the critical technical interface between simulation/modeling and autonomy consumers. By ensuring model fidelity, coverage, and usability, the team plays a role in evaluating and advancing autonomous driving software. We support massive-scale, parallel execution for scaled verification and validation (V&V) and high-throughput reinforcement learning (RL) training. Ultimately, we provide the framework and tooling that serves Autonomy, Safety, and Release with shared artifacts, common metrics, and deterministic reproducibility. Our culture is collaborative, energetic, and team focused.
Key Responsibilities
- Own and evolve the technical architecture for scalable vehicle modeling and simulation — extensible across vehicle types, sensor platforms, and fleet variability as our autonomy program grows.
- Develop and own high-fidelity truck and vehicle dynamics models (longitudinal, lateral, and transient behavior) spanning nominal, off-nominal, and failure-mode simulation domains.
- Define and drive the statistical validation metrics that directly support Safety Case claims, ensuring model fidelity and coverage meet the bar required for driver-out certification.
- Design and scale robustness testing frameworks — including controller/planner interaction testing — to stress-test autonomy software at the fidelity and throughput required for large-scale V&V and RL training.
- Design, develop, and maintain high-fidelity simulation platforms used to validate autonomous driving software in closed-loop evaluation pipelines at scale.
- Partner with autonomy engineering teams to capture and implement vehicle model requirements supporting planning, controls, and system-level V&V activities.
- Own model capability communication, known limitations, and release notes to enable effective autonomy validation across consuming teams.
- Ensure vehicle model updates do not regress autonomy V&V system performance, using automated regression frameworks you help define.
- Execute full software development lifecycle activities primarily in C++ within a Linux environment and ROS/ROS2 tooling, applying Lean-Agile methodologies.
- Perform root cause analysis on complex issues surfaced in simulation runs and hardware-in-the-loop testing.
- Drive test plan design for data acquisition and telemetry supporting field data collection and vehicle model refinement.
- Set direction for system-level test plans and verification strategies across the simulation organization.
- Communicate technical direction, design decisions, and blockers clearly at stand-ups, design reviews, and cross-organization architecture discussions.
- Build and maintain collaborative relationships with OEM partners and simulation tool vendors to evaluate, integrate, and co-develop simulation capabilities.
- Mentor engineers across the simulation and autonomy domains and help raise the bar on code quality, process, and testing rigor.
Required Qualifications
Education: Bachelor's Degree in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, or a related technical field plus 10+ years of relevant experience; or Master's Degree in the above fields plus 7+ years; or PhD plus 3+ years.
Experience: A track record of driving technical consensus across simulation, autonomy, controls, product, and safety teams is expected. This role will help set direction, not just execute against it. Operates as an advanced-level professional with latitude for independent judgment and minimal supervision. A track record of mentoring engineers and contributing to technical direction within the simulation and autonomy domains.
Technical Skills: Deep proficiency in C++ (primary), Python for tooling, ROS/ROS2, CMake, and Linux. A strong background in physics-based modeling of ground vehicles, including longitudinal/lateral dynamics, tire models, and powertrain. Working knowledge of AV autonomy stack architecture — planning, controls, and system integration. Demonstrated ability to translate vehicle model capabilities and limitations to autonomy engineering teams, and to capture their requirements to inform model fidelity improvement initiatives. Experience defining statistical validation metrics and robustness testing frameworks used to support formal safety case claims. Experience with unit, integration, and regression testing, automated validation pipelines, and simulation-based performance benchmarking at scale. Ability to own and maintain key technical systems across multiple repositories and contribute to cross-organization architectural decisions.
Preferred Qualifications
- Experience as technical lead for a vehicle simulation sub-team, driving architecture across vehicle types, sensor platforms, and fleet variability.
- Experience with high-fidelity truck & trailer or heavy-vehicle models.
- Experience building or extending closed-loop autonomous vehicle simulation environments at scale.
- Familiarity with scenario-based validation and sim-to-real correlation activities.
- Familiarity with how learned models (neural networks) consume simulation and vehicle model outputs in autonomy V&V workflows.
Everforth Apex Systems is an equal opportunity employer. We do not discriminate or allow discrimination on the basis of race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), age, sexual orientation, gender identity, national origin, ancestry, citizenship, genetic information, registered domestic partner status, marital status, disability, status as a crime victim, protected veteran status, political affiliation, union membership, or any other characteristic protected by law. Everforth Apex will consider qualified applicants with criminal histories in a manner consistent with the requirements of applicable law.
