Software Engineer, Systems Generalist

Posted 8 Days Ago
Be an Early Applicant
San Francisco, CA, USA
Hybrid
350K-475K Annually
Entry level
Artificial Intelligence • Information Technology
The Role
Build and scale infrastructure supporting frontier AI model training, research, and product development. Responsibilities may include operating Kubernetes clusters with GPU workloads, developing data pipelines with Spark, improving developer productivity tooling, debugging across application, operating system, and network layers, and delivering reliable distributed systems end to end.
Summary Generated by Built In
About Thinking Machines

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 Role

We’re looking for generalist infrastructure and systems engineers to help build the systems that power our foundation models and the internal teams on research and product development to be able to create the models and ship the products powered by our models.

You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind everything we do. You’ll work across the full technical stack, solving complex distributed systems problems and building robust, scalable platforms.

Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, improve infrastructure efficiency, and enable key insights across our models, products, and data assets.

What You’ll Do

We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the infrastructure teams where they'll have the greatest impact and growth potential.

Here are example areas you may contribute to depending on your area of expertise and interest:

  • Core Infrastructure: We support teams that train, research, and ultimately serve AI models and build the underlying infrastructure for the clusters to reliably and safely train frontier models. Examples might include building systems and running large Kubernetes clusters with GPU workloads, or building infrastructure to support Tinker.

  • Data Infrastructure: We build and maintain the data systems for our research and products. You'll design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies.  You’ll build scalable, reliable, data infrastructure while embedding governance best practices.

  • Developer Productivity: We care deeply about research and engineering productivity and our ability to continue shipping quickly. We build tooling, systems, frameworks, and systems to make sure everyone gets well configured, optimized developer environments.

Skills and QualificationsMinimum 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
  • Strong debugging across application, OS, and network layers.

  • Proficiency in Python or Rust (or similar), containers, and modern CI.

  • Experience with Kubernetes, controllers/operators, or performance profiling.

  • Familiarity with GPU/ML workflows or large‑scale data/eval pipelines.

Logistics
  • 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 related 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
  • Ability to collaborate with cross-functional partners and subject matter experts
  • Initiative to work across different technology stacks and teams
  • Strong debugging skills across application, operating system, and network layers
  • Proficiency in Python or Rust, containers, and modern continuous integration
  • Experience with Kubernetes, controllers or operators, or performance profiling
  • Familiarity with GPU or machine learning workflows or large-scale data and evaluation pipelines
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The Company
HQ: Singapore
91 Employees

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.

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