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Senior MLOps Engineer

Senior MLOps Engineer

Posted 4 days ago

Job Description

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.

Core 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

Required Qualifications

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)

Preferred Qualification

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

Bonus Points

Experience with autonomy or robotics

Edge deployment constraints (latency, power, thermal)

Data versioning tools (DVC, LakeFS)

Teleo is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. All qualified people are encouraged to apply.

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