MLOps Engineer

We are seeking a Staff MLOps Engineer to join an innovative Series B robotics and automotive technology startup in Woburn, MA. This is a full-time opportunity for a senior machine learning infrastructure engineer who wants to own the production lifecycle of computer vision models powering real-world automotive service applications. You’ll work across Google Cloud Platform, Docker, Python, PyTorch/Tensor Flow, model serving, CI/CD, infrastructure as code, and scalable inference systems.

This is a true ownership role where you’ll take an existing machine learning product from MVP to production scale. You’ll own the architecture behind how computer vision models are deployed, monitored, evaluated, improved, and served to customers. The technology is already being used in real automotive service environments, so your decisions will have a direct impact on product performance, customer experience, and the company’s ability to scale.

You’ll also serve as a senior cloud architecture voice alongside the broader engineering team, making this a great opportunity for someone who wants significant technical ownership without stepping away from hands-on engineering.

• 8+ years of professional engineering experience
• Several years of experience owning machine learning systems in production
• Proven experience taking computer vision or ML pipelines from prototype through production scale
• Deep experience with segmentation and classification models, preferably in multi-stage pipelines
• Strong cloud infrastructure experience, preferably with Google Cloud Platform
• Experience with Vertex AI, Cloud Run, GKE, Cloud Functions, Cloud SQL, Pub/Sub, and Docker
• Strong Python development skills
• Production experience with PyTorch or Tensor Flow
• Hands-on experience with model serving and optimization
• Experience with technologies such as Triton, Torch Serve, ONNX, TensorRT, quantization, or similar platforms
• Experience owning production deployments, incident response, rollback procedures, and on-call support
• Experience building data collection, labeling, and dataset versioning workflows
• Experience developing reproducible model evaluation and regression testing systems
• Experience with Terraform or similar infrastructure-as-code tools
• Experience with CI/CD automation such as Git Hub Actions
• Strong understanding of the trade-offs between model accuracy, latency, infrastructure cost, and reliability
• Excellent debugging, problem-solving, and communication skills

• Experience with on-device or edge inference
• Experience with Core ML, Tensor Flow Lite, or Execu Torch
• Experience building active learning or human-in-the-loop labeling systems
• Computer vision experience with smaller, long-tail, or industrial inspection datasets
• Experience integrating cameras, sensors, or other hardware with ML systems
• Robotics, IoT, or edge computing experience
• Experience with ROS or similar robotics platforms
• Experience integrating ML capabilities into mobile applications
• Automotive service, dealership, DMS, or automotive technology experience
• Experience working in an early-stage or high-growth startup
• Open-source contributions
• Experience collaborating closely with hardware and field operations teams

• Own the architecture and operation of the company’s multi-stage computer vision inference pipeline
• Re-architect the current MVP infrastructure into a scalable production serving platform
• Design containerized inference infrastructure with GPU acceleration, queueing, batching, and autoscaling where appropriate
• Own model deployment, versioning, staged rollouts, canary testing, shadow evaluation, and rollback
• Build the data and model improvement lifecycle from field data collection through labeling, training,…