Data Infra Engineer: Scale Distributed Data Pipelines

Mosaic.tech; San Francisco (CA); On-site; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines is seeking an engineer to help design and scale the core data infrastructure behind distributed training pipelines and multimodal data catalogs. You’ll work with researchers to accelerate experiments, improve data quality, and build systems that scale across petabytes of data.

Ideal candidates have hands-on Python or Rust experience and comfort with Spark or Ray, cloud data lakes, and end-to-end project ownership, in a collaborative, fast-moving environment in San Francisco.

Qualifications

• Bachelor’s degree or equivalent in computer science, engineering, or similar.
• Proficiency in Python or Rust.
• Fluent in distributed compute frameworks such as Apache Spark or Ray.
• Familiar with cloud infrastructure, data lake architectures, and batch/streaming pipelines.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment with cross-functional partners.
• Proactive with initiative to work across stacks to ship.

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.

Job description

Thinking Machines is seeking an engineer to help design and scale the core data infrastructure behind distributed training pipelines and multimodal data catalogs. You’ll work with researchers to accelerate experiments, improve data quality, and build systems that scale across petabytes of data.

Ideal candidates have hands-on Python or Rust experience and comfort with Spark or Ray, cloud data lakes, and end-to-end project ownership, in a collaborative, fast-moving environment in San Francisco.