Robotics Engineer

General Robotics•Redmond, WA

1d•$155,000 - $205,000

About The Position

General Robotics is building the future of general robot intelligence. Our mission is to enable the rapid, robust, and safe deployment of general intelligence for autonomous systems and robotics. Our core platform, GRID, provides a comprehensive library of robot skills through a fast, cloud-native runtime designed to make any robot intelligent—including aerial, wheeled, humanoid, quadruped, and robotic arms. We are seeking Robotics Engineers to join our team to integrate our technology onto physical hardware and lead customer deployments. This role offers a front-row seat to the transformative impact of Physical AI and the opportunity to collaborate with a world-class team of AI and robotics pioneers.

Requirements

Master’s degree (completed or in progress) in Robotics, Computer Science, or a related technical field, or equivalent practical experience.
Current and ongoing work authorization in the country of employment.
2+ years of experience in the development and implementation of robotics, machine learning, or control algorithms.
Strong proficiency in robotics hardware/software stacks (e.g., ROS/ROS2) and programming languages (e.g., Python, C++).
Demonstrated experience implementing and evaluating end-to-end robot learning systems.
Ability to travel to customer sites as needed.

Nice To Haves

Proven track record of building and deploying controllers, data acquisition pipelines, and machine learning or classical robotics algorithms on multiple robotic platforms.
Experience utilizing constrained edge compute devices (e.g., NVIDIA Jetson) for data acquisition and model inference.
Experience handling sensitive data and adhering to strict security protocols.

Responsibilities

System Integration: Independently set up robots for real-world deployments, including integrated edge compute and sensor suites.
Model Deployment: Deploy state-of-the-art AI models and techniques on physical hardware; lead field deployment and evaluation efforts.
Experimental Design: Design, execute, and analyze experiments with rigorous, well-defined hypotheses.
Data Engineering: Design data collection routines to acquire high-quality data from field tests for model fine-tuning.
Technical Literacy: Maintain a deep understanding of model architectures and their effects on real-world performance, specifically regarding computer vision and machine learning from a robotics perspective.
Systems Optimization: Account for common systems constraints—such as bandwidth, compute, and latency—when making modeling choices. Identify and implement optimization opportunities to improve performance on edge compute devices.
Cross-functional Collaboration: Work closely with AI and Simulation Engineers to transition new techniques to the field; communicate critical insights and metrics from real-world testing.
Platform Growth: Translate field-tested patterns and learnings into scalable, streamlined capabilities within the GRID platform.