Senior MLOps Engineer

Teleo • Palo Alto, CA

1d • $200,000 - $250,000

About The Position

Teleo is a robotics startup disrupting a trillion-dollar industry. Teleo converts construction heavy equipment, like loaders, dozers, excavators, trucks, etc. into autonomous robots. This technology allows a single operator to efficiently control multiple machines simultaneously, delivering substantial benefits to our customers while significantly enhancing operator safety and comfort. Teleo is founded by Vinay Shet and Rom Clément, experienced technology executives who led the development of Lyft’s Self Driving Car and Google Street View. Teleo is backed by YCombinator, Up Partners, F-Prime Capital, and a host of industry luminaries. Teleo’s product is already deployed on several continents and generating revenue. Teleo is poised for rapid growth. This presents a unique opportunity to be part of a team that is creating a product with a profound impact on our customers, working on cutting-edge 100,000-pound autonomous robots, engineering intricate systems at the intersection of hardware, software, and AI, and joining the early stages of an exciting startup journey. About the Role Own the reliability, scalability, and velocity of model training and deployment for autonomy systems. Turn experimental models into dependable production services.

Requirements

• 2+ years in MLOps / Infra / ML Platform
• Deep experience with PyTorch, CUDA-aware workflows
• Strong Linux + systems fundamentals
• Proven experience deploying models at scale (not just notebooks)

Nice To Haves

• Training orchestration: Ray, Slurm, Kubernetes, Airflow
• Model lifecycle: Weights & Biases, MLflow, custom registries
• Containers: Docker, multi-arch builds
• Inference optimization: TensorRT, ONNX, Triton
• Monitoring: metrics, logs, alerts for ML systems
• Experience with autonomy or robotics
• Edge deployment constraints (latency, power, thermal)
• Data versioning tools (DVC, LakeFS)

Responsibilities

• Design and operate end-to-end ML infrastructure: training, evaluation, deployment, monitoring
• Build CI/CD for ML (model versioning, promotion, rollback, canarying)
• Own model observability: drift detection, performance regression, data health
• Optimize GPU utilization across training and inference (on-prem + cloud)
• Support edge deployment (Jetson / Orin / x86 + GPU)
• Work closely with perception and autonomy teams to reduce friction from research to production

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed