Principal AI Engineer, Closed-Loop Control

4MP Inc is building the intelligence layer for precision manufacturing. The Principal AI Engineer will design and build AI systems for closed-loop control in manufacturing, working closely with the founding team to develop practical solutions that integrate real-world sensor data and machine behavior.

Responsibilities:

• Design and build AI systems that connect sensor data, machine behavior, manufacturing context, and closed-loop correction
• Work directly with the founding team on systems that interact with real machines, real sensors, and real manufacturing data
• Sensor intelligence — calibration, fusion, uncertainty quantification, and drift detection
• Physical AI modeling — machine behavior, error modeling, and physics-informed learning
• Manufacturing context — interpreting CNC programs, machining intent, and process state
• Closed-loop correction — learning systems that improve from real correction-to-outcome feedback
• Scaling intelligence — transferring learned knowledge across machines and deployments

Requirements:

• 6+ years in AI/ML with substantial work in control, optimization, reinforcement/imitation learning, or inverse problems
• Experience turning measured error or state into corrective action — closed-loop systems where model outputs change physical behavior
• Strong optimization and control foundation (numerical optimization, MPC, or learned control)
• Comfort working against real, noisy feedback — drift, delay, partial observability, and safety constraints
• Strong Python and production-grade ML engineering (PyTorch)
• Mathematical maturity in optimization, control theory, dynamics, and probability
• Degree in CS, Robotics, EE/ME, Physics, or Applied Math — or equivalent demonstrated work
• Reinforcement / imitation learning for control ; differentiable simulation
• Model-predictive control , trajectory optimization, system identification
• Differentiable physics / rendering ; physics-informed learning (PINNs)
• Uncertainty-aware decision-making — Bayesian methods, conformal prediction, risk-aware control
• Working with geometric / 3D representations as input to control (bridges to the perception side)
• CNC / machining physics : tool deflection, thermal error, material removal, fixturing
• CAM, G-code, toolpath generation
• M.S. or Ph.D. in control, robotics, optimization, or related