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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.\nAt 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.\nKey responsibilities\nLead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence\n\nDefine and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives\n\nEstablish best practices, standards, and processes for ML infrastructure, deployment, and operations\n\nOwn technical decision-making for ML infrastructure architecture and tooling choices\n\nArchitect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks\n\nPartner 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\n\nCollaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions\n\nDesign and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data\n\nCollaborate with research teams to bridge the gap between experimentation and production\n\nDefine logging, alerting, and monitoring strategy to track model performance, drift, and system reliability\n\nMust have\nBachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)\n\n7+ years of experience in MLOps, with 5+ years in a technical leadership role\n\nStrong software engineering skills in Python, with experience in Bash and/or Go\n\nProven track record of building and leading high-performing MLOps or infrastructure teams\n\nExperience building and designing MLOps infrastructure from the ground up\n\nDeep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.)\n\nDeep experience with model 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