Principal ML Ops Engineer
Posted Apr 21, 2026
Location: Cambridge, MA (Eastern Time / UTC -4) Relocation package available or Remote option for Out-Of-State applicants Start date: ASAP Languages: English (required)
What Youll Do
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Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. - Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters. - Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows). - Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost). - Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models. - Collaborate with researchers to productionize models and accelerate training/inference pipelines. - Establish ML Ops best practices, internal standards, and cross-team tooling. - Mentor engineers and influence architectural direction across the entire AI platform. ## What Are Looking For
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Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected). - Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure). - Proficiency with Python and familiarity with TypeScript or Go for platform integration. - Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration (practical understanding)