Senior Robot Perception Engineer Visual Inspection MLOps

Bright Machines is a San Francisco-based AI-enabled manufacturer that uses robotics, software, and production data to assemble AI and data-center infrastructure.

San Francisco, United States
About Bright Machines

Bright Machines operates the Bright Factory manufacturing platform, combining virtual product development, AI-enabled robotics, and factory intelligence for data-center infrastructure assembly and traceability.

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Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will develop, optimize, and deploy visual-inspection algorithms and deep-learning models for production automation. You will build training and evaluation data pipelines, optimize GPU inference, establish MLOps practices, monitor deployed models, design inspection metrics, and support customer rollouts.

Requirements

  • MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience
  • 5+ years of computer vision or machine learning experience
  • Python programming skills
  • PyTorch experience
  • Experience optimizing ML models for production GPU inference
  • Experience shipping ML or computer-vision models to production
  • Experience with image acquisition, camera systems, and sensor integration

Responsibilities

  • Develop and optimize visual-inspection algorithms for defect detection, anomaly detection, classification, and quality validation
  • Optimize model inference for GPU deployment using CUDA and TensorRT
  • Collaborate on illumination setups for inspection accuracy and robustness
  • Build and maintain data pipelines for model training, evaluation, and improvement
  • Establish MLOps practices for model versioning, experiment tracking, retraining, and monitoring
  • Harden inspection solutions through monitoring, alerting, and graceful degradation
  • Deploy inspection solutions and support customer rollouts
  • Define metrics and benchmarks for inspection accuracy, throughput, and reliability