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.
About the RoleWe’re looking for an engineer to design, build, and operate the GPU supercomputing environment that powers large‑scale training and inference. You will deliver high‑performant, reliable, and cost‑efficient compute so our users and researchers can move fast at scale.
Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.
What You’ll DoOperate and automate large GPU clusters including provisioning, imaging, and capacity planning.
Write software that abstracts cluster management and presents a unified interface for training and inference.
Extend scheduling/orchestration (Kubernetes, Slurm, or similar) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy.
Monitor and improve operational metrics of speed, reliability, and error recovery.
Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage.
Partner with researchers to unblock scale runs and advise on parallelism and performance trade‑offs.
Minimum qualifications:
Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
Proficiency in at least one backend language (we use Python or Rust).
Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).
Comfort operating across the stack and owning projects end-to-end.
Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
Preferred qualifications — we encourage you to apply if you meet some but not all of these:
Strong systems background: Linux, networking, and infrastructure‑as‑code.
Familiarity with CUDA/NCCL and performance profiling for distributed training/inference.
Prior work supporting large‑scale model training or inference environments.
Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures.
Track record of working in fast-paced environments balancing care with urgency.
Location: This role is based in San Francisco, California.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Skills Required
- Bachelor’s degree or equivalent experience in computer science, engineering, or a similar field
- Proficiency in at least one backend programming language, such as Python or Rust
- Experience operating large-scale clusters and container orchestration systems, such as Kubernetes or Slurm
- Ability to work across the technology stack and own projects end-to-end
- Strong collaboration skills across cross-functional teams and subject matter experts
- Initiative and bias for action across different technology stacks and teams
- Strong systems background in Linux, networking, and infrastructure-as-code
- Familiarity with CUDA, NCCL, and performance profiling for distributed training or inference
- Experience supporting large-scale model training or inference environments
- Understanding of deep learning frameworks such as PyTorch, TensorFlow, or JAX and their system architectures
- Experience working in fast-paced environments while balancing care with urgency
What We Do
Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable. We are scientists, engineers, and builders who've created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
