AI Systems Engineer

Target Candidate Level: Master’s Degree (Recent graduate or early-career professional)

Location: Remote, some travel involved

The AI Systems Engineer is responsible for evaluating the safety, stability, and robustness of autonomous systems across both physical hardware and virtual environments. This role involves developing mathematical models, physical tests, and adversarial evaluation suites to expose failure modes in multi-modal perception systems, robotic stability controllers, and virtual AI agents.

The ideal candidate possesses a strong foundation in mathematical modeling and dynamic simulation, with the ability to validate and calibrate models against real-world empirical data.

• Formulate and implement adversarial models and perturbation scenarios targeting perception pipelines across multiple modalities.
• Characterize sensor failure modes caused by physical degradation, environmental interference, and edge-case disturbances.
• (Broad familiarity with sensing systems is expected; deep specialization in all individual sensor physics is not required).

• Develop structured adversarial evaluations for virtual AI systems, decision-making pipelines, and autonomous agent architectures.
• Identify failure points related to policy exploitation, logic drift, and multi-step reasoning vulnerabilities.

• Build and maintain mathematical models and physics-based simulations representing system behavior under nominal and perturbed conditions.
• Design physical experiments to collect ground-truth validation data.
• Calibrate simulation parameters against real-world test data to ensure high-fidelity sim-to-real transfer.

• Collaborate with autonomy, perception, and controls teams to deliver quantitative failure assessments.
• Work effectively within an iterative, fast-paced engineering environment.

• Education: Master of Science (M.S.) in Robotics, Electrical Engineering, Mechanical Engineering, Applied Mathematics, Computer Science, or a related quantitative field.
• Mathematical Modeling & Simulation: Strong competency in mathematical modeling, dynamic systems, and calibrating simulations using empirical real-world data.
• Control Systems: Foundational understanding of robotic stability controllers, state estimation, and dynamic system response.
• AI & Autonomy: Working knowledge of modern machine learning principles, embodied AI concepts, and agentic workflows.
• Programming: Proficiency in Python for numerical modeling, simulation scripting, and data processing.
• Physical Testing: Ability to design and analyze hands-on physical tests to validate analytical models.
• Execution: Demonstrated capability to deliver results collaboratively in a fast-paced development cycle.

• Experience leveraging modern AI-assisted engineering and development tools (e.g., Cursor, Claude, Gemini) to augment workflow efficiency and code development.
• Familiarity with software engineering fundamentals (e.g., Git version control, modular code design).
• Exposure to adversarial machine learning concepts, robustness benchmarks, or uncertainty estimation.
• Hands-on experience with robotic test benches, instrumentation, or sensor data acquisition.
• Compensation
  : $105,000–$135,000 base salary, depending on experience and location, plus eligibility for our annual bonus program.
• Benefits
  : medical, dental and vision coverage, 401(k) with a Safe Harbor company match, and unlimited PTO.
• Applications accepted through January 15, 2027.

Fennec Engineering helps the companies that build and deploy robots and industrial automation make systems people can trust, through the ASAP Platform for safety engineering, ASAP Testing for physical validation, and a team of functional safety engineers. We’re based in Columbus, Ohio.