Director, Product Management – ML Platform
New
United StatesFull-TimeDirector
Salary239000 - 320000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- Distributed Systems
Requirements
- 8+ years of product management experience
- At least 3 years focused on ML infrastructure, backend systems, or data platforms
- 2+ years in people management
- Strong technical depth in machine learning infrastructure, including model pipelines, feature stores, distributed systems, batch/streaming architectures, and production-scale deployment
- Proven ability to operate as a player-coach, balancing strategic roadmap ownership with hands-on technical execution and detailed product work
- Experience partnering with engineering, data science, and finance teams to align on infrastructure priorities, cost optimization, and system performance
- Strong analytical thinking and stakeholder management skills, with the ability to prioritize competing demands across multiple technical teams
- Excellent communication skills, able to translate complex technical concepts into clear, actionable direction for both technical and non-technical audiences
- Degree in Computer Science, Engineering, or related technical field; advanced degree preferred
Responsibilities
- Own the end-to-end product vision, strategy, and roadmap for the ML Platform, including backend infrastructure supporting large-scale model training, deployment, and iteration cycles.
- Lead, mentor, and develop a team of Product Managers while driving a player-coach approach across strategic planning and execution.
- Drive platform initiatives such as batch and streaming infrastructure, reliability and uptime improvements, cost visibility, and system hardening for production-scale ML systems.
- Partner closely with ML and engineering teams to translate technical constraints and requirements into prioritized, executable roadmaps and product specifications.
- Collaborate with Finance and business stakeholders to define, track, and optimize infrastructure cost efficiency and resource allocation.
- Define and monitor platform KPIs including reliability, cost performance, and model enablement metrics to drive continuous improvement.
- Establish scalable product management frameworks and operating models for ML platform development and long-term organizational growth.
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