Senior Product Manager, TetraScience

• 7+ years of Technical Product Management experience, specifically within cloud infrastructure, backend services, or developer platforms
• Deep understanding of the ML Lifecycle: You should be intimately familiar with the infrastructure requirements for both model training (e.g., job scheduling, distributed compute) and inference (e.g., autoscaling, REST/gRPC APIs)
• Infrastructure Fluency: Strong background in container orchestration (Kubernetes), cloud providers (AWS/Azure), and CI/CD pipelines
• Platform Mindset: A track record of building "internal products" or APIs where the primary customer is a developer or a data scientist
• Education: Bachelors or Masters degree in Computer Science, Engineering, or a related technical field

Desirable

• In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO
• This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective
• It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building
• If you join us, you will be expected to embody its contents each day
• The Senior Product Manager, Model Infrastructure & Execution Services will lead the strategy for how we orchestrate, deploy, and monitor machine learning workloads
• You will own the "Compute & Execution" layer of our platform, ensuring that scientific teams can move from raw data to a trained model, and finally to a production-grade inference endpoint, with zero friction
• Your mission is to build a world-class Developer Experience (DX) for ML and AI
• You will focus on the "plumbing" that makes AI possible: elastic training environments, high-performance inference services, and the critical metadata layers (lineage and observability) that ensure scientific reproducibility in a regulated environment
• This is a platform role
• You aren’t building the models; you are building the high-scale machinery that allows Biopharma enterprises to develop and run them at the scale of Petabytes
• Dual Service Strategy (Inference & Training): Define the roadmap for two core service pillars:
• Training Services: Orchestrating elastic, cost-optimized compute (GPU/CPU) for model training and experiment tracking
• Inference Services: Managing the deployment of models into high-availability, low-latency API endpoints
• Ease of Development & Deployment: Radicalize the user experience for ML Engineers. You will build self-service "push-button" deployment workflows that abstract away the complexity of Kubernetes and cloud networking
• Lineage & Reproducibility: Ensure every model has a clear "paper trail." You will define how we capture the lineage between data versions, training code, and production artifacts—a critical requirement for Biopharma compliance
• Observability & Governance: Build the tools to monitor model health in production. This includes infrastructure-level metrics (latency/memory) and model-level observability (drift/performance) to ensure system reliability
• Technical Stakeholder Engagement: Partner with Scientific IT and Platform Engineering to ensure our services integrate seamlessly with existing enterprise identity (IAM) and security frameworks
• Backlog & Execution: Act as the "CEO of the Service," translating complex infrastructure needs into clear, actionable epics and user stories for a high-performing engineering team