Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.

As a Staff Robotics Systems Integration Engineer, you’ll own the hardware and systems health of our humanoid robot fleet, and shape how our hardware strategy evolves as we scale.

This is an early, high-ownership seat. You won’t inherit a roadmap, you’ll help write it. You’ll set technical direction for fleet reliability, hardware qualification, calibration, and test infrastructure, and influence hardware decisions across mechanical, electrical, firmware, and controls.

It’s also genuinely hands-on. The same person who sets the long-term reliability strategy should be comfortable pulling an arm off a robot, probing a suspect driver board, soldering a fix, and designing the bracket that makes the repair stick. You’ll sit at the intersection of hardware, firmware, controls, and data, and you’ll be the person the team calls when a robot does something nobody can explain.

This is a full-time, on-site position in Redwood City.

What You’ll Do

Hardware Roadmap: Define and drive the hardware reliability and integration priorities based on fleet data and customer impact.

Hardware Strategy: Turn field evidence into input for next-generation actuator selection, electrical architecture, thermal design, and serviceability, and make the case for those changes with mechanical, electrical, and firmware partners.

Qualification & Release Standards: Establish qualification, validation, and release practices (EVT/DVT/PVT-style gates, incoming test, pre-merge validation).

Root-Cause Analysis: Actuator dropouts, encoder and calibration drift, thermal events, CAN bus faults, and control-stack bugs, separating true hardware degradation from firmware, calibration, and model-side causes.

Hands-On Hardware Work: Bench-test, rework, and repair actuators and electronics, and design and build the fixtures, jigs, brackets, and small modifications that go with them. Turn evidence on backlash, wear, preload, and hard-stop behavior into concrete design changes, in close partnership with the mechanical team.

Fleet Health Systems: Health metrics and alerting tuned to real faults rather than noise floors, calibration and characterization tooling with safety gates, and telemetry pipelines for bandwidth-constrained robots (MCAP, npz, episode archives).

Code Review & Documentation: Review PRs across firmware, control code, and deployment config, and write runbooks, SOPs, and incident reports clean enough that a teammate can pick the work up cold.

What You’ll Bring

The Experience: 7+ years in robotics, mechatronics, hardware test, or reliability engineering, with significant time on fielded hardware rather than lab prototypes.

Technical Leadership: A track record of owning technical direction: setting priorities, making hardware tradeoff calls, and influencing partner teams without formal authority.

Comfort with Ambiguity: You’d rather define the problem and the plan than wait for them.

Hands-On Instincts: Soldering iron, multimeter, calipers, and the ability to tear down, repair, and re-commission electromechanical assemblies without waiting on another team.

Actuator Mechanics: Deep familiarity with how actuators fail mechanically (backlash, wear, preload loss, end-stop damage), and enough design sense to hold your own in a design review.

Robot Controls: Working knowledge of robot control (impedance and torque-controlled actuators, controller state lifecycles) to recognize when a “hardware” fault is actually a controls bug.

Actuators & Telemetry: Fluency with quasi-direct-drive actuators, encoders, torque and current telemetry, CAN, and calibration and zeroing procedures.

Python & Data Analysis: Strong Python for data analysis and tooling, with solid time-series instincts around noise floors, drift, and persistence rules, plus experience with Datadog or similar and PR-based workflows.