Tech Lead Manager, Baseten

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience
• 5+ years of experience in ML infrastructure, distributed systems, or ML platform engineering, including 2+ years in a tech lead or manager role
• Strong expertise in distributed training frameworks and orchestration (FSDP, DDP, ZeRO, Ray, Kubernetes, Slurm, or similar)
• Hands-on experience building or scaling training infrastructure for LLMs or other foundation models
• Deep understanding of GPU/accelerator hardware utilization, mixed precision training, and scaling efficiency
• Proven ability to lead and mentor technical teams while delivering complex infrastructure projects
• Excellent communication skills, with the ability to bridge technical depth and business needs

Desirable

• As a Tech Lead Manager of the Training team at Baseten, you’ll lead a team of engineers building the core systems that power large-scale training and fine-tuning of foundation models
• Your team will be responsible for designing scalable, reliable, and efficient infrastructure - covering distributed training frameworks, GPU scheduling, and training pipelines—enabling both Baseten and our customers to train and adapt models at scale
• You’ll balance hands-on technical contributions with people management, setting the technical direction while fostering the growth and success of your team
• You’ll also play a key role in defining Baseten’s platform roadmap by identifying common infrastructure needs and turning them into reusable, self-serve capabilities
• Lead, mentor, and grow a team of engineers building Baseten’s training infrastructure
• Define and drive the technical strategy for large-scale training systems, with a focus on scalability, reliability, and efficiency
• Architect and optimize distributed training pipelines across heterogeneous GPU/accelerator environments
• Balance hands-on contributions (system design, code reviews, prototyping) with people leadership and career development
• Establish best practices for training workflows, distributed systems design, and high-performance model evaluation
• Collaborate with Product and Platform Engineering to translate customer and internal needs into reusable infrastructure and APIs
• Develop processes that ensure consistent, reliable, and on-time delivery of high-quality systems
• Stay ahead of the curve on advancements in training efficiency (FSDP, ZeRO, parameter-efficient training, hardware-aware scheduling) and bring them into production