Safeguards ML Infrastructure Engineer for Scalable Research

United States Digital Space LLC; San Francisco (CA); On-site; USD 350,000 - 500,000; Full time

Job summary

United States Digital Space LLC seeks an engineer to own the infrastructure behind Safeguards research, building tooling, pipelines, and interfaces that accelerate experiments and improve reliability.

You will work between research and production, solving large-scale systems challenges while expanding machine learning expertise and ensuring the systems stay aligned with policy commitments.

Qualifications

• Strong software engineering fundamentals and Python proficiency.
• Experience building and operating data-intensive or distributed systems in production.
• Experience building tooling or infrastructure used as a dependency by other engineers.
• Comfort working across the research-to-deployment pipeline, from experiments to production systems.
• Ability to debug performance and correctness across unfamiliar stacks.
• Strong written and verbal communication and collaborative decision making.

Responsibilities

• Build and scale the infrastructure and data pipelines behind Safeguards research
• Own training, evaluation, and scoring workflows with a focus on reducing cycle time
• Design tooling and interfaces researchers can use directly without deep systems knowledge
• Build correctness and sanity checks into the stack for trustworthy results
• Take high-value research workflows from experiments to production-grade jobs
• Improve throughput, cost, and reliability of large-scale inference and scoring workloads
• Partner with researchers and engineers across Safeguards to anticipate needs

Skills

Python

Distributed systems

Tooling

Research-to-prod pipeline

Education

Bachelor's degree

Tools

Experiment tracking

Caching layers

Evaluation harnesses

Job description

United States Digital Space LLC seeks an engineer to own the infrastructure behind Safeguards research, building tooling, pipelines, and interfaces that accelerate experiments and improve reliability.

You will work between research and production, solving large-scale systems challenges while expanding machine learning expertise and ensuring the systems stay aligned with policy commitments.