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About the Role

Build libraries and tools to accelerate research, owning internal infrastructure like evaluation and RL training libraries, and experiment tracking platforms. This collaborative role involves working directly with researchers to identify and resolve bottlenecks, ensuring systems are trusted and delightful to use.

Responsibilities

• Design, build, and operate research infrastructure including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.
• Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.
• Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, implementing monitoring and observability.
• Partner directly with researchers to identify bottlenecks and unlock new capabilities, owning research tooling and tracking adoption.
• Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack.

Requirements

• Bachelor's degree or equivalent experience in computer science, engineering, machine learning, or similar.
• Strong software engineering fundamentals with a track record of building reliable, maintainable systems.
• Proficiency in at least one backend language (Python or Rust).
• Comfort operating across the stack and owning projects end-to-end.
• Experience in highly collaborative environments involving cross-functional partners and subject matter experts.
• Track record building tooling for researchers that achieved high adoption without top-down mandates.
• Experience building or maintaining ML research infrastructure such as training frameworks, evaluation libraries, or experiment tracking systems.
• Contributions to open-source ML tools or widely-used internal frameworks at research-focused organizations.
• Record of publications or technical writing on ML systems, infrastructure, or tooling.
• Background working closely with ML researchers to understand and solve their tooling needs.
• Familiarity with distributed systems, modern ML frameworks (PyTorch, JAX), and data processing at scale.
• Experience with research observability tools, distributed compute frameworks (Ray, Spark), or large-scale evaluation pipelines.

Skills

• Software engineering fundamentals
• Python
• Rust
• Full-stack operation
• End-to-end project ownership
• Collaboration
• Building tooling for researchers
• ML research infrastructure
• Training frameworks
• Evaluation libraries
• Experiment tracking systems
• Open-source ML tools
• Technical writing on ML systems
• Technical writing on infrastructure
• Technical writing on tooling
• Understanding ML researcher tooling needs
• Distributed systems
• PyTorch
• JAX
• Data processing at scale
• Research observability tools
• Ray
• Spark
• Large-scale evaluation pipelines

Location

• San Francisco, California
• New York, NY

Education Level

• Bachelor's degree or equivalent experience in computer science, engineering, machine learning, or similar

Salary/Compensations

• $350,000 - $475,000 USD

Benefits

• Generous health benefits
• Generous dental benefits
• Generous vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support
• Visa sponsorship

About the Company

• Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence.
• Building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
• Team comprises scientists, engineers, and builders who created widely used AI products (ChatGPT, Character.ai), open-weights models (Mistral), and popular open-source projects (PyTorch, OpenAI Gym, Fairseq, Segment Anything).

Equal Opportunity

• Does not discriminate on the basis of any protected group status under any applicable law, as set forth in Thinking Machines' Equal Employment Opportunity policy.
• Considers qualified applicants with criminal histories consistent with the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.