Senior Product Manager, ML Platform

Role Overview

• Lead the planning and prioritization of ML platform initiatives, balancing cross-team impact and advancing model training, deployment, monitoring, and scalability.
• Define and operationalize success metrics for platform performance, system monitoring and adoption.
• Own the prioritization of work and decisions on the systems that support LLM workflows (prompt iteration, retrieval, fine-tuning).
• Experienced with API-based LLM usage where iteration, speed and eval loops matter most.
• Partner closely with Data Science and ML Engineering, with a strong emphasis on the Machine Learning lifecycle.
• Drive stakeholder alignment across complex data systems (Airflow pipelines, LLM/agentic tooling) and communicate broader business impact.
• Contribute to evolving modeling strategies, productizing and generalizing the work to be consumable and accelerate growth across teams.
• Prioritize and enable cutting edge research and experimentation against product and business goals.
• Write, standardize, and maintain product and technical documentation.
• Develop a roadmap of product enhancements, features, and solutions to continue to innovate and evolve the platform.
• Identify opportunities to enable teams to move more quickly by understanding constraints and trade-offs.
• Lead cross functional team of engineers, data scientists, and product managers through the product development lifecycle from inception to launch of new features.
• Communicate your strategic vision for the platform to cross functional stakeholders, working closely with leadership to ensure alignment.
• Understand interactions and impact across multiple products and decide the best strategic path forward.

Requirements

• 2-3+ years of product management experience, focused on internal technical product management
• 2-3+ years working with Data Science and ML products and tooling
• Experience building internal and platform tools and features that serve a full product suite
• Ability to translate highly technical work and requirements into business goals and impact, and vice versa
• Experience working at an early-stage company or similarly fast-paced environment
• Awareness of GPU constraints (job scheduling, cost, GPU availability)
• Experience working cross-functionally with engineering, data science, and other technical teams to drive product success
• Experience managing both external and internal (Customer Success, Implementations, etc.) stakeholders

Tech Stack

• Airflow