MLOps Lead

Key Responsibilities

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence.
• Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives.
• Establish best practices, standards, and processes for ML infrastructure, deployment, and operations.
• Own technical decision-making for ML infrastructure architecture and tooling choices.
• Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks.
• Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe.
• Define inference architecture strategy optimized for ultra-low latency and high throughput.
• Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data.
• Collaborate with research teams to bridge the gap between experimentation and production.
• Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
• 7+ years of experience in MLOps, with 3+ years in a technical leadership role.
• Strong software engineering skills in Python, with experience in Bash and/or Go.
• Proven track record of building and leading high-performing MLOps or infrastructure teams.
• Experience building and designing MLOps infrastructure from the ground up.
• Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.).
• Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference.
• Experience building and managing data pipelines to support both model training and inference.
• Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps).
• Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry).
• Excellent communication skills with ability to translate between research and production contexts.

Nice-to-Haves

• Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows).
• Experience with FastAPI and backend applications.
• Familiarity with data platforms like Databricks or Snowflake.
• Experience with LLM/foundation model serving and optimization.
• Exposure to SRE practices or cloud security certifications.
• Experience scaling ML infrastructure for AI startups.

Benefits

• Competitive compensation with salary and equity.
• Comprehensive health coverage for you and your dependents.
• Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys.
• Relocation support for employees moving to join the team in one of our office locations.
• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action.