AI Systems Engineer

Position Type: Full-Time

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

Location: Remote, some travel involved

Department: Engineering / Autonomy & Systems Robustness

Role Overview

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 can model and simulate how systems behave over time, then validate and calibrate those models against empirical measurements.

Key Responsibilities

Perception & Sensor Robustness

• 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.

Robotic Control & Dynamical Stability Testing

• Design adversarial test methodologies to evaluate the stability limits of robotic control systems.

• Model and inject disturbances, including parameter mismatches, latency, and changing environmental forces, to identify control boundaries and instability regimes.

Virtual AI & Agent Adversarial Testing

• 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.

Simulation, Physical Testing & Model Calibration

• 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 physical test data so the simulations accurately represent the systems under test.

Cross-Functional Collaboration

• Collaborate with autonomy, perception, and controls teams to deliver quantitative failure assessments.

• Work effectively within an iterative, fast-paced engineering environment.

Qualifications

• 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 of systems over time and calibration of simulations against empirical measurements.

• Control Systems: Understand robotic stability controllers and state estimation, including how systems respond over time.

• 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.

Preferred Qualifications

• 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.

How to Apply

Please submit your resume along with a brief summary of relevant academic or professional projects demonstrating your experience in dynamics modeling, physical experimentation, or system robustness evaluation to careers@fennec-engineering.com.