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 Role
You’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 For

• 5+ 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, SQL
• Experience with recommendation systems, ranking, or personalization
• Strong DevOps fundamentals

What Doesn’t Work

• Pure data scientists (model-only, no infra)
• Data engineers without ML lifecycle ownership
• Candidates who haven’t owned or architected MLOps systems

Why This Role

• High 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