ML Ops Data Scientist (Remote)

Lead MLOps Data Scientist (Remote, US) $170-206K base + equity We're working with a high-growth, profitable consumer platform that's already driven meaningful revenue impact through machine learning and is now investing heavily in scaling its ML infrastructure. This is a foundational hire — you'll own and rebuild the company's MLOps platform end-to-end, enabling faster experimentation, deployment, and real-world impact across a strong data science team. The RoleYou'll act as the technical owner of MLOps, leading the design and build of next‐gen infrastructure that powers model training, deployment, and iteration. Architect and build a 0→1 MLOps platform Overhaul CI/CD pipelines for ML workflows Reduce model deployment timelines from weeks → days Build systems for feature stores, retraining, and experimentation Partner closely with Data Science & Engineering to scale ML impact Stay hands‐on (70–80% coding) What They're Looking For5+ years in MLOps / ML Infrastructure Proven experience owning end‐to‐end ML pipelines in production Experience building from 0→1 OR deep ownership in a mature ML platform Strong background in:ML orchestration (Kubeflow, Vertex AI, Airflow, etc.) CI/CD for ML systems Cloud (GCP preferred), Python, SQLExperience with recommendation systems, ranking, or personalization Strong DevOps fundamentals What Doesn't WorkPure data scientists (model‐only, no infra) Data engineers without ML lifecycle ownership Candidates who haven't owned or architected MLOps systems Why This RoleHigh impact: ML already drives the business — this scales it further Ownership: true 0→1 platform build Lean, strong team: ~5 data scientists delivering real results Remote‐first with strong comp + equity#J-18808-Ljbffr