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.

Key Responsibilities

• 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

Requirements

• 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

Preferred Requirements

• Working hours in Eastern Time Zone
• Experience with MLOps frameworks (e.g., Kubeflow, MLflow, or SageMaker) at a Series B-D scale
• Knowledge of Infrastructure-as-Code (Terraform) and observability stacks (Prometheus/Grafana/Datadog)
• Background in Life Sciences or Biopharma, understanding the nuances of GxP or regulated data environments

Benefits

• 100% employer-paid benefits for all eligible employees and immediate family members
• Unlimited paid time off (PTO)
• 401K
• Flexible working arrangements - Remote work
• Company paid Life Insurance, LTD/STD
• A culture of continuous improvement where you can grow your career and get coaching

We are not currently providing visa sponsorship for this position