Staff Mechanical Engineer, Technical Lead

Build the Path Forward

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together

We're looking for a Technical Lead to guide our team's contribution to Path's manipulation products as we expand from autonomous welding into factory automation. On our Sensing and Embodied Intelligence team, you'll set direction for sensing end-of-arm tooling, the physical interfaces between robots, tools, sensors, and parts, and the tasks we design to train manipulation models.

This is a hands-on leadership role. You'll define architecture, make the hard tradeoff calls and build hardware yourself.

What You'll Do

Technical Direction

• Own the technical roadmap for sensing end-of-arm tooling, physical interfaces, and training task design

• Translate product goals and model training needs into hardware requirements and interface specifications

• Lead design reviews and set standards for robustness, serviceability, and manufacturability

• Identify technical risks early and retire them before they reach the field

Sensing End-of-Arm Tooling

• Lead the design of end-of-arm tooling that integrates cameras, 3D, force/torque, tactile, and proximity sensors

• Define sensor placement, calibration, and mounting that hold accuracy under vibration, heat, spatter, dust, and repeated contact

• Drive tooling from concept through production release

Physical Interfaces

• Define mechanical, electrical, and data interfaces between robots, tools, tool changers, sensors, fixtures, and workpieces

• Establish standards that let tools and sensors be swapped, serviced, and reused across platforms

• Align physical interfaces with hardware abstraction, calibration, and control architecture

Task Design for Model Training

• Design the tasks, fixtures, parts, and environments used to collect demonstration and training data

• Define task variation, difficulty, and success criteria that represent real industrial work

• Partner with ML engineers to connect task design to model performance