Systems Engineer - Machine Learning
General Robotics5 months ago
Base Salary
$155k - $200k/yr
Responsibilities
- Integrate and productionize state-of-the-art ML models in serving infrastructure while collaborating with research teams.
- Develop infrastructure tooling that makes onboarding new models faster and more reliable.
- Develop and maintain low-latency, high-throughput ML inference pipelines for robotics workloads.
- Optimize GPU workloads and accelerate ML frameworks through data-transfer, memory-management, batching, serialization, and concurrent-request optimizations.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience.
- At least 1 year of experience in ML infrastructure, model serving, or backend systems engineering.
- Strong Python skills and the ability to turn unfamiliar research codebases into production services.
- Familiarity with PyTorch, JAX, Docker, Kubernetes, and distributed serving frameworks such as Ray or Triton.
- Familiarity with async Python, real-time communication protocols, and robotics systems is a plus.
- Familiarity with AWS, GCP, Azure, and infrastructure-as-code tooling is preferred.
- Must obtain and maintain work authorization in the country of employment; the role is open to candidates currently authorized to work in the United States.
Benefits
- Medical, 401(k), and other health benefits are included.
- The role is based in Redmond, Washington, and is open to candidates currently authorized to work in the United States.
Categories
About General Robotics
General Robotics is an AI research and deployment company building the intelligence grid for physical AI. We bring modular, adaptable intelligence to every robot, across any form, task, or environment. Our mission: making every robot useful, fast. We believe general intelligence emerges from rich composition of robot skills — not just larger models. By combining modular AI skills, we enable robots to sense, reason, and act with precision. Our approach is data-efficient, interpretable, and built for safety-critical use.