Software Engineer, Research Acceleration

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. 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. About the Role We’re looking for engineers to build the libraries and tools that accelerate research at Thinking Machines. You’ll own internal infrastructure — evaluation libraries, RL training libraries, experiment tracking platforms — and build systems that compound research velocity over time. This is a collaborative role. You will work directly with researchers to identify bottlenecks and pain points. Success means researchers trust your systems to just work and find them a delight to use. What You'll Do 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. Implement monitoring and observability. Partner directly with researchers to identify bottlenecks and unlock new capabilities. Own research tooling like a product manager, proactively seeking feedback and tracking adoption. Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack. Skills and Qualifications Minimum qualifications: Bachelor's degree or equivalent experience in computer science, engineering, machine learning, or similar. Strong software engineering fundamentals with a track record of building r