MLOps Engineer
Core
Building reproducible, reliable ML systems that connect experimental models to production, focusing on training pipelines, data infrastructure, evaluation, deployment, and monitoring for computer vision and spatial perception models.
Role type
MLOps Engineer (Computer Vision & Spatial Perception)
Builds
Production-grade ML infrastructure, data pipelines, model deployment workflows, and internal tooling for experiment tracking and monitoring.
Domain
Computer Vision, Spatial Perception, Robotics
Deliverable
production ML models
Required skills
Python, Software Design, Production ML Infrastructure, PyTorch, Model Deployment & Optimization, Experiment Tracking, CI/CD, Monitoring, Data Pipelines
Preferred skills
C++, Distributed Training, Edge/Embedded Deployment, Agentic Infrastructure
Technologies
PyTorch, CUDA, ONNX
Responsibilities
Build and maintain training, evaluation, and deployment infrastructure; Develop systems for ingesting and validating real-world video and sensor data; Create monitoring for model quality, runtime performance, and data drift; Support efficient use of local and cloud compute; Improve experiment tracking; Build internal tools for data inspection and regression analysis; Contribute to engineering standards around reliability and release quality.
