Master's degree in Robotics, Computer Science, or similar, or equivalent practical experience.
2+ years in robotics, machine learning, or control algorithms.
Proficiency with robotics stacks (e.g., ROS/ROS2) and programming (e.g., Python, C++).
Experience with end-to-end robot learning systems.
Ability to travel to customer sites when necessary.
Responsibilities
Set up robots for real-world deployments with integrated edge computing and sensors.
Deploy AI models on hardware and lead field evaluation efforts.
Design and analyze experiments with clear hypotheses.
Create data collection routines for high-quality data acquisition.
Maintain knowledge of model architectures impacting performance, particularly in computer vision.
Optimize systems by addressing constraints like bandwidth and latency while improving performance.
Collaborate with AI and Simulation Engineers to implement techniques in the field.
Benefits
Medical insurance coverage.
401K plan.
Other health benefits.
Full Job Description
We are seeking Robotics Engineersto 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.
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.
Minimum 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.
Preferred Qualifications
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.
Base Salary Range
The anticipated base salary for this position is $155,000 - $205,000. Your actual pay will be based on factors such as skills, experience, and location. In addition to base salary, this role is eligible for benefits including medical, 401K and other health benefits.
Work Authorization
This role is open to candidates currently based in and authorized to work in the US.
