Software Engineer, Data Infrastructure

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. is seeking an engineer to join our data infrastructure team to design and operate scalable systems for distributed training pipelines and data catalogs. You will work with researchers to accelerate experiments and build from the ground up.

You’ll develop high-throughput ingestion and processing pipelines, implement quality controls, and ensure observability across the data lifecycle, collaborating across teams to ship impact at scale.

Qualifications

• Bachelor’s degree in computer science, engineering, or a related field.
• Proficiency in at least one backend language (Python or Rust).
• Fluent with distributed compute frameworks such as Apache Spark or Ray.
• Deep familiarity with cloud infrastructure, data lake architectures, and batch/streaming pipelines.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a collaborative environment with cross-functional partners.
• Bias for action and initiative to ship solutions across stacks.

Responsibilities

• 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

Python

Rust

Distributed compute frameworks (Spark/

Cloud infrastructure

Data lake architectures

Batch and streaming pipelines

End-to-end ownership

Collaboration

Initiative across stacks

Education

Bachelor's degree in Computer Science / Engineering

Tools

Kafka

dbt

Terraform

Airflow

Job description

The mission of Thinking Machines is to build AI that extends human will and judgment.

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. 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.

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 —

• 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, 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.