Senior Engineering Manager ML Platform

Skills

About the Role

You will lead the ML platform’s technical direction while contributing hands-on to infrastructure design and implementation. You will scale GPU compute, distributed training, data storage, retrieval, and data pipelines. You will establish platform architecture, improve reliability and developer productivity, partner with ML stakeholders, manage incidents, hire and mentor engineers, and create scalable operating processes.

Requirements

• 7–12 years of engineering experience
• 2–3 years in a formal management or tech lead capacity
• Experience building or scaling a platform, infrastructure, or ML systems team
• Technical credibility in GPU or distributed compute infrastructure, large-scale data storage and retrieval, or data pipeline frameworks
• Experience making foundational architectural decisions
• Cross-functional communication skills
• Hands-on coding, design review, and production debugging ability

Responsibilities

• Own the strategy, roadmap, and execution for GPU compute infrastructure
• Design and implement platform infrastructure
• Improve reliability, performance, and cost efficiency across distributed training clusters
• Evaluate compute hardware and cloud or on-premises infrastructure
• Oversee data storage, indexing, retrieval, and data pipelines
• Establish standards for data access, lineage, and governance
• Lead shared libraries and frameworks for data transformation pipelines
• Make architectural decisions for compute orchestration, storage, and pipeline frameworks
• Partner with ML researchers, engineers, and product teams
• Communicate roadmaps, incidents, and technical tradeoffs
• Hire, mentor, and develop engineers
• Define on-call, incident-response, and engineering processes

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k)
• Paid time off
• Annual bonus structure