Founding Edge Intelligence Lead | Robotics & Actuation

Eccentric Machines is building Sentor, an AI-native motion platform for robotics and complex electromechanical systems. The Founding Edge Intelligence Lead will build the onboard machine learning and decision-making layer, developing real-time sensing, state estimation, adaptive control, embedded inference, and simulation-to-real capabilities for physical machines.

Responsibilities:

• You will develop models and algorithms that interpret Sentor’s local sensor data in real time
• Torque resistance estimation
• Load and contact inference
• Vibration and resonance mode prediction and detection
• Friction / hysteresis pattern recognition
• Thermal and mechanical drift prediction and detection
• Fatigue / degradation signatures
• Sensor fusion across current, position, velocity, torque, temperature, strain, vibration, and related signals
• You will build the onboard intelligence layer that enables Sentor to respond intelligently to changing physical conditions
• Lightweight ML models for low-power embedded inference
• Adaptive torque / speed / stiffness response
• Local reflex-like behaviors
• Learned response profiles
• Anomaly detection and protective behaviors
• Real-time classification of operating regimes
• Prediction of near-term load or resistance changes
• Safe local adaptation within guardrails
• You will work on algorithms that can coexist with, augment, or inform control systems rather than fight them
• Model predictive control
• Adaptive control
• Reinforcement learning for constrained physical systems
• Imitation learning
• System identification
• State-space models
• Kalman filtering / particle filtering
• Residual learning around physics-based models
• Learned compensators for friction, hysteresis, backlash, vibration, or load variation
• You will work closely with our simulation / digital twin function
• Generate synthetic operating scenarios
• Test edge models before hardware deployment
• Simulate load changes, impacts, failures, and edge cases
• Train or validate models in virtual environments
• Compare simulated sensor traces with real bench data
• Accelerate sim-to-real iteration
• Develop motion profiles before physical customer deployments
• You will help turn ML models into deployable real-time software
• Embedded C model development and deployment
• Model compression and optimization
• Embedded inference
• Latency and memory optimization
• Real-time data pipelines
• Firmware / embedded software integration
• Safety fallbacks
• Deterministic behavior under constrained compute
• Deployment to microcontrollers, embedded Linux, DSPs, or edge accelerators
• Building the first sensor data pipeline for Sentor
• Defining the onboard intelligence architecture
• Identifying which sensor signals are most predictive of physical state
• Creating initial models for load / resistance / operating-regime inference
• Deploying early edge models or algorithms on prototype hardware
• Building safety-bounded local response behaviors
• Integrating with bench-test data and simulation outputs
• Helping define how Sentor learns from repeated physical interaction