Founding Edge Intelligence Lead | Robotics & Actuation

Seniority: Senior / Principal / Founding Engineer

Compensation: Competitive cash compensation, benefits, and founding-engineer level equity package. This is one of the company’s first technical hires and will carry meaningful ownership, technical authority, and long-term upside tied to building a foundational part of the Sentor platform.

The company is building , a patent-pending AI-native motion platform for robotics and complex electromechanical systems.

The AI-native motion platform is designed to move intelligence closer to the physical motion layer. Instead of treating actuation as a passive output from a central controller, we are building hardware and software that can sense local conditions, adapt to changing loads, and respond in real time.

We recently closed our seed round and are building the founding technical team.

We are looking for a Founding Edge Intelligence Lead to build the onboard machine learning and decision-making layer for the AI-native motion platform. This role sits at the intersection of machine learning, robotics, controls, embedded systems, and physical hardware. Your job is to turn signals such as load changes, vibration patterns, motion signatures, thermal behavior, and other physical signals into useful intelligence: state estimation, anomaly detection, adaptive response, and eventually learned motion behavior.

This is not a cloud ML role. It is not a dashboard role. It is not a generic AI role.

It is the software layer that lets a physical machine learn from its own body.

You will develop models and algorithms that interpret Sentor’s local sensor data in real time.

This may include:

• load and contact inference;
• vibration and resonance mode prediction and detection;
• friction / hysteresis pattern recognition;
• thermal and mechanical drift prediction and detection;
• sensor fusion across current, position, velocity, torque, temperature, strain, vibration, and related signals.

The goal is to help AI-native motion platform to understand what is happening locally before waiting for a centralized robot controller.

You will build the onboard intelligence layer that enables AI-native motion platform to respond intelligently to changing physical conditions.

This may include:

• 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.

The system must be fast, robust, and suitable for real-time actuation. Model elegance matters less than useful, reliable behavior on hardware.

This role must bridge ML and controls.

You will work on algorithms that can coexist with, augment, or inform control systems rather than fight them.

Relevant areas may include:

• adaptive control;
• reinforcement learning for constrained physical systems;
• imitation learning;
• system identification;
• residual learning around physics-based models;
• learned compensators for friction, hysteresis, backlash, vibration, or load variation.

The right person understands that onboard intelligence in an actuator needs to be safe, bounded, and physically grounded.

You will work closely with our simulation / digital twin function.

Over time, the edge intelligence layer should interface with simulation tools so we can:

• test edge models before hardware deployment;
• simulate load changes, impacts, failures, and edge cases;
• train or validate models in virtual environments;
• compare simulated sensor…