Baseten
This range is provided by Baseten. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
$250,000.00/yr - $300,000.00/yr ABOUT BASETEN
Baseten powers inference for the world's most dynamic AI companies, like OpenEvidence, Clay, Mirage, Gamma, Sourcegraph, Writer, Abridge, Bland, and Zed. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. With our recent $150M Series D funding, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction, we’re scaling our team to meet accelerating customer demand. THE ROLE
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. RESPONSIBILITIES
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 REQUIREMENTS
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, DPP, 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 NICE TO HAVE
Experience with multi-tenant, production-grade ML platforms Familiarity with cluster management, GPU scheduling, or elastic resource scaling Knowledge of advanced model adaptation techniques (LoRA, QLoRA, RLHF, DPO) Contributions to open-source distributed training or ML infrastructure projects Experience building developer-friendly APIs or SDKs for ML workflows Cloud-native infrastructure experience (AWS, GCP, Azure, containerization, orchestration) BENEFITS
Competitive compensation package. This is a unique opportunity to be part of a rapidly growing startup in one of the most exciting engineering fields of our era. An inclusive and supportive work culture that fosters learning and growth. Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
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$250,000.00/yr - $300,000.00/yr ABOUT BASETEN
Baseten powers inference for the world's most dynamic AI companies, like OpenEvidence, Clay, Mirage, Gamma, Sourcegraph, Writer, Abridge, Bland, and Zed. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. With our recent $150M Series D funding, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction, we’re scaling our team to meet accelerating customer demand. THE ROLE
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. RESPONSIBILITIES
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 REQUIREMENTS
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, DPP, 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 NICE TO HAVE
Experience with multi-tenant, production-grade ML platforms Familiarity with cluster management, GPU scheduling, or elastic resource scaling Knowledge of advanced model adaptation techniques (LoRA, QLoRA, RLHF, DPO) Contributions to open-source distributed training or ML infrastructure projects Experience building developer-friendly APIs or SDKs for ML workflows Cloud-native infrastructure experience (AWS, GCP, Azure, containerization, orchestration) BENEFITS
Competitive compensation package. This is a unique opportunity to be part of a rapidly growing startup in one of the most exciting engineering fields of our era. An inclusive and supportive work culture that fosters learning and growth. Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
#J-18808-Ljbffr