Software Engineer, Data Infrastructure

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 an engineer to join us and contribute to data infrastructure. You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind distributed training pipelines, multimodal data catalogs, and intelligent processing systems that operate over petabytes of data. Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets. If you're excited by distributed systems, large-scale data mining, open-source tools like Spark, Kafka, Beam, Ray, and Delta Lake, and enjoy building from the ground up, we'd love to hear from you. 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 Do Design, build, and operate scalable, fault-tolerant infrastructure for LLM Research: distributed compute, data orchestration, and storage across modalities. Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search. Build systems for traceability, reproducibility, and robust quality control at every stage of the data lifecycle. Implement and maintain monitoring and alerting to support platform reliability and performance. Collaborate with research teams to unlock new features, improve data quality, and accelerate training cycles. Skills and Qualifications 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). Are fluent in distributed compute frameworks such as Apache Spark or Ray. Are deeply familiar with cloud infrastructure, data lake architectures, and batch and streaming pipelines. 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: Have hands-on experience with Kafka, dbt, Terraform, and Airflow. Have experience building a web crawler. Have extensive experience understanding and scaling deduplication, data mining, and search. Have strong knowledge of file formats and storage systems (e.g., Parquet, Delta Lake, etc.) and how they impact performance and scalability. Are proactive about documentation, testing, and empowering your teammates with good tooling. Logistics Location: This role is based in San Francisco, California. Compensation: Depending on background, skills and experience, th