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