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Technical Program Manager (Model Performance)

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Onsite

Technical Program Manager (Model Performance)

Baseten

Technical Program Manager (Model Performance)

Skills

Deep LearningR

2 months ago

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Important information


Contract type:

Permanent

Salary:

$165,000 – $330,000 / year

Location:

United States

Work mode:

Onsite

Published on:

3 September 2026

What they need


  • The Model Performance organization at Baseten is looking to hire our first Technical Program Manager
  • This is a zero-to-one role in a team that is responsible for building the core algorithms and methods that power Baseten’s high performance inference stack
  • You won’t inherit an existing program framework, you’ll build one from the ground up: the planning structure, execution processes, metrics and the cross-functional alignment that a fast-growing organization needs
  • Your contributions will directly impact how fast our performance R&D gets productized
  • If you can drive turning a set of ambitious but loosely defined initiatives into a predictable, well-governed program, this role is for you
  • Own execution across Model Performance’s active project portfolio, freeing the team’s technical leads to focus on technical direction rather than tracking
  • Design and stand up the planning structures, operating cadences, and status reporting mechanisms that best fits the team’s DNA
  • Coordinate model release and optimization programs end to end, including day-zero launches, sequencing the work across performance engineering, infra, and release stakeholders
  • Drive cross-team alignment as scope expands from Model Performance Core into Model APIs and the inference production stack (BIS)
  • Surface risks and dependencies early, and keep leadership informed with clear, honest status
  • Partner with engineering leads to design team structures and ownership boundaries as the org scales

Benefits

  • Remote-first work environment. The Baseten team is welcome to work from wherever they want; fully remote, in our San Francisco office, or a mix of both. Today, our team (including our founding team) is spread across the United States, Canada, and Armenia. We provide a $1,000 stipend for you to make your home-office comfortable and productive
  • Regular in-person team summits. We get together as a team three times a year to plan, workshop, and most importantly, get to know each other better
  • Unlimited PTO. We ask that everyone take at least 4 weeks of vacation. And we have a company-wide break between Christmas and New Year’s Day
  • Full healthcare coverage. Medical, dental and vision insurance for you and your family
  • Paid parental leave. 16-weeks fully paid parental leave (adoptive and non-birth parents included) and flexibility with schedules while returning to work
  • Company-sponsored 401(k) for you to contribute to
  • Learning and development budget. We encourage you to take classes, attend conferences, and invest in your craft and we’ll cover expenses to make it happen- High agency decision making that demonstrates ownership, accountability and a strong desire to get things done
  • Experience program-managing model performance or inference optimization work - you understand how engines like vLLM, TensorRT-LLM, SGLang, or NVIDIA Dynamo fit into a production serving stack and can engage credibly with the engineers building on them
  • Proven ability to influence without authority across engineers, managers, and leadership who don’t report to you
  • Deep technical program management experience - you’re already running programs of this scope at an organization of similar or greater complexity
  • Excellent written and verbal communication, with the ability to make deeply technical programs legible to any audience
  • Demonstrated comfort with ambiguity and zero-to-one program building, not just executing an existing framework
  • Deep learning performance optimization background, in training or inference, with inference preferred
  • Prior hands-on software engineering background
  • Experience coordinating model release programs, including day-zero launches (ideally for large-scale models)

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