Machine Learning Infrastructure Engineer, Safeguards Research

The shape of it

In the posting’s own words

We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change.

What it asks for · 6

• Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python
• Experience building and operating data-intensive or distributed systems in production
• Experience building tooling or infrastructure that other engineers or researchers use as a dependency
• Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems
• Ability to debug performance and correctness problems across an unfamiliar stack
• Strong written and verbal communication skills, and a collaborative approach to technical decisions

Also a plus

• Experience with high-performance, large-scale machine learning systems
• Familiarity with language modeling and transformers, including working with model internals
• Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization
• Experience building experiment tracking, caching layers, or evaluation harnesses for research teams
• Experience with probes, interpretability, or classifier development
• Interest in the misuse risks of AI systems and a desire to work on mitigating them

What the job covers

• Build and scale the infrastructure and data pipelines behind Safeguards machine learning research
• Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result
• Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath
• Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve
• Take the highest-value research workflows from experiments to reliable, production-grade jobs
• Improve the throughput, cost, and reliability of large-scale inference and scoring workloads
• Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time

Tools and skills named

Words the posting leans on

• systems9×
• experience6×
• research6×
• researchers6×
• machine learning5×
• models5×
• build4×
• detection4×
• experiments4×
• problems4×
• tooling4×
• depend3×
• engineers3×
• experience building3×
• infrastructure3×
• large-scale3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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