Position OverviewWe are seeking a Systems Engineer to join our team in Redmond, WA. We build and optimize the infrastructure that powers generalized robotics foundation models - spanning edge devices to cloud GPU clusters - with a focus on low latency, high throughput, and efficient resource utilization across form factors such as aerial, ground, manipulation, and others.
We are looking for strong candidates who have a background in systems engineering and AI/ML infrastructure, with experience in areas like systems programming; distributed systems architecture; GPU-accelerated computing; containerized deployments; and ML training and inference optimization. By applying to this role, you will be considered for multiple teams, such as platform infrastructure, ML systems, and edge deployment.
Systems Engineer Responsibilities- Design, build, and optimize systems infrastructure spanning edge devices to cloud GPU clusters for robotics workloads.
- Develop and maintain low-latency, high-throughput pipelines for ML model training and inference.
- Architect and manage distributed systems for efficient resource utilization across heterogeneous compute environments.
- Optimize GPU/CUDA workloads and accelerate ML frameworks (PyTorch, JAX, TensorFlow) for robotics applications.
- Build and maintain containerized deployment pipelines using Kubernetes and Docker.
- Collaborate with research teams to translate model requirements into scalable, production-grade systems.
- Design monitoring, profiling, and benchmarking tools to identify and resolve performance bottlenecks.
- Contribute to infrastructure tooling and open-sourcing efforts.
Qualifications- Bachelor's degree in Computer Science, Computer Engineering, Robotics, relevant technical field, or equivalent practical experience. Master's degree preferred.
- 1+ years of experience in systems engineering, software engineering, robotics, or AI/ML systems.
- Strong systems programming skills in one or more of: C++, Rust, Go, Python.
- Solid understanding of operating systems, networking, and distributed systems fundamentals.
- Experience with ML frameworks (PyTorch, JAX, TensorFlow) and cloud infrastructure (Kubernetes, Docker).
- Experience with GPU programming and CUDA optimization for ML workloads.
- Experience designing and scaling distributed training infrastructure for large foundation models.
- Familiarity with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code tooling.
- Familiarity with real-time systems, embedded platforms, or edge deployment for robotics.
- Experience with high-performance networking, storage systems, or scheduler design (e.g., Slurm, Ray).
- Good understanding of ML model architectures and the ability to factor systems constraints into research decisions.
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
Base Salary RangeThe 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 AuthorizationThis role is open to candidates currently based in and authorized to work in the US.
