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About the Role

Own compute allocation and optimize the use of supercomputing resources by working closely with researchers and infrastructure engineers. This role involves understanding demand, evaluating competing needs, translating research priorities into practical allocation decisions, and teaching teams to plan and use compute effectively.

Responsibilities

Own compute allocation, including understanding demand, developing plans, and executing decisions.
Optimize resource usage by working through tradeoffs across capacity, hardware, workload size, timing, and dependencies.
Improve productive use of compute by partnering with researchers and engineers to identify opportunities for better scheduling and utilization.
Teach teams to plan and use compute effectively by helping them estimate needs, understand options, and recognize consequences.
Make reasoning transferable by explaining assumptions and tradeoffs, and developing guidance and tools.
Connect research and infrastructure by grounding research plans in usable capacity and communicating future needs.
Learn from outcomes by comparing expected and actual usage, revisiting assumptions, and improving decisions.

Requirements

Experience making consequential resource allocation or capacity decisions in a complex computing environment with clear ownership of tradeoffs and outcomes.
Strong technical understanding of distributed systems, machine learning infrastructure, or high-performance computing.
Ability to reason about how workload requirements interact with system constraints.
Demonstrated ability to improve useful output from limited resources.
Ability to teach complex technical ideas through clear explanations and practical examples.
Comfort investigating usage and performance data directly, questioning assumptions, and changing views based on evidence.
Sound judgment when teams have competing priorities, earning trust, explaining difficult decisions, and working constructively through disagreement.
A hands-on, collaborative approach with strong attention to detail and follow-through.
Experience owning compute allocation at an AI research lab or similar organization.
Familiarity with GPU clusters, distributed training, scheduling, and workload placement.
Understanding of how networking, storage, reliability, and hardware differences affect usable capacity.
Experience building tools or analyses for capacity forecasting, allocation, or utilization.
Experience mentoring researchers, engineers, or technical program managers in compute planning and resource management.

Skills

Resource allocation
Capacity planning
Distributed systems
Machine learning infrastructure
High-performance computing
Technical teaching
Data investigation
Problem-solving
Collaboration
Attention to detail
GPU clusters
Distributed training
Scheduling
Workload placement
Networking
Storage
Reliability
Hardware differences
Capacity forecasting
Mentoring

Location

San Francisco, California

Work Type

Full-time

Experience Level

Mid-level
Senior-level

Salary/Compensations

[Add approved salary range.]

Benefits

Health, dental, and vision coverage
Unlimited PTO
Paid parental leave
Relocation support

About the Company

The mission of Thinking Machines is to build AI that extends human will and judgment.
We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication.
We believe the future worth building is human, and we're hiring people who want to build it.