MLOps Lead
Lead and mentor an MLOps team to build scalable ML infrastructure, automated pipelines, and low-latency model serving for large tabular models. Requires 7+ years MLOps experience including 3+ years leading teams, deep expertise in Kubernetes, model serving frameworks, and observability tools.
About the job
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.
Skills
MLOps, Python, Kubernetes, Terraform, Helm, PyTorch, TensorFlow, MLflow, Wandb, Triton, Torchserve, Kserve, Prometheus, Grafana, Datadog
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