fundamental - MLOps Team Lead
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 • 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
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
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
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