🚀 Hiring: ML Infrastructure Engineer – $150k–$230k (San Jose) Min 3 years experience post graduation

I'm supporting a cutting-edge robotics company building large-scale, real-time ML systems for autonomous platforms. They’re looking for an ML Infrastructure Engineer to strengthen the backbone that powers their perception, planning, and simulation workloads.

What you’ll work on:

• Build and maintain high-performance training and inference pipelines

• Design distributed systems for large-scale data processing, versioning, and model tracking

• Deploy and optimize PyTorch/TensorFlow models on embedded, edge, and cloud environments

• Streamline experiment workflows: dataset tooling, model evaluation, automated retraining

• Develop internal ML tooling enabling faster iteration for research and autonomy teams

• Collaborate closely with robotics, vision, simulation, and systems engineers

What you’ll bring:

• 5+ years in ML infrastructure, ML Ops, robotics ML systems, or similar

• Strong Python + production-grade experience with ML frameworks (PyTorch, TensorFlow)

• Deep experience with distributed systems, GPUs, containerization, and orchestration (Docker, Kubernetes)

• Experience with data engines (Ray, Spark, Dask), internal ML tooling, or scalable pipelines

• Understanding of embedded/edge deployment, optimization (TensorRT, ONNX, CUDA) is a plus

• Solid communication and cross-team collaboration skills

📍 San Jose, CA | Onsite | $150k–$230k + stock

If you want to work on high-impact autonomy systems and build the infrastructure that powers the entire ML stack, feel free to reach out.