Fundamental is hiring a MLOps Team Lead

Fundamental is looking for an MLOps Team Lead to lead and mentor a team of MLOps engineers, define the strategic roadmap, and architect scalable ML infrastructure and pipelines. You will bridge the critical gap between research and production in our mission-driven, low-ego environment.

What You'll Do

• 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

What We're Looking For

• 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 Have

• 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

Technical Stack

• Languages: Python, Bash, Go
• MLOps Platforms: MLflow, WandB
• ML Frameworks: PyTorch, TensorFlow
• Model Serving: Triton, TorchServe, TensorFlow Serving, KServe
• Infrastructure: Kubernetes, AWS, GCP, Azure, Terraform, Helm, GitOps
• Observability: Prometheus, Grafana, Datadog, OpenTelemetry
• Orchestration & Tools: Kubeflow, Airflow, Argo Workflows, FastAPI, Databricks, Snowflake

Team & Environment

You will lead and mentor a team of MLOps engineers. The company culture is mission-driven and low-ego, valuing diversity of thought, ownership, and bias toward action.

Benefits & Compensation

• Competitive compensation with salary and equity
• Comprehensive health coverage, including medical, dental, vision, and 401K
• Fertility support
• 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

We are an equal opportunity employer.