About the role
About Fundamental Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by 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 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 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 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
