Systems Engineer - Machine Learning

General Robotics•Redmond, WA

2d•$155,000 - $200,000

About The Position

General Robotics is an AI research and deployment company building a platform for general robot intelligence. Our mission is to enable rapid, robust, and safe deployment of general intelligence for autonomous systems and robotics. We aspire to become the starting point for AI-powered autonomous systems across a diverse set of scenarios. Position Overview We are seeking an ML Engineer to join our team in Redmond, WA. We build and optimize the platform that serves ML models to robots in real-time — from perception and planning to foundation models — with a focus on low latency, high throughput, reliable and robust robot-to-cloud communication. We are looking for strong candidates who have a background in ML infrastructure and model serving, with experience in areas like CUDA kernel programming; distributed serving frameworks; real-time streaming; and taking research models to production. By applying to this role, you will be considered for multiple teams, such as platform infrastructure, ML systems, and edge deployment.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, or relevant technical field, or equivalent practical experience.
1+ years of experience in ML infrastructure, model serving, or backend systems engineering.
Strong Python.
Comfortable navigating unfamiliar research codebases and turning them into clean, production services.
Familiarity with ML frameworks (PyTorch, JAX), containerized deployments (Docker, Kubernetes), and distributed serving frameworks (Ray, Triton, or similar).
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.

Nice To Haves

Familiarity with async Python, real-time communication protocols, and robotics systems is a plus.
Familiarity with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code tooling.

Responsibilities

Integrate and productionize state-of-the-art ML models into our serving infrastructure, collaborating with research teams to bring new architectures from prototype to deployment.
Contribute to infrastructure tooling that makes onboarding new models faster and more reliable.
Develop and maintain low-latency, high-throughput pipelines for ML model inference across robotics workloads.
Optimize GPU workloads and accelerate ML frameworks for real-time performance: data transfer, memory management, batching, serialization, and concurrent request handling.