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

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks

• Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.

• Develop scalable inference architectures optimized, with ultra-low latency and high throughput

• Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities

• Develop logging, alerting, and monitoring solutions to track model development, and reliability

• Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency

• Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

Must have

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

• 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)

• Experience building and designing MLOps infrastructure from the ground up

• Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference

• Experience in building and managing data pipelines to support both model training and inference

• Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)