About the role
About Fundamental
Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.
At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.
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
- Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream
- Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions
- 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
Must have
- 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 5+ 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
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