Machine Learning Infrastructure Engineer

Roles and Responsibilities:\n- Build and scale the infrastructure and data pipelines behind Safeguards machine learning research\n- Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result\n- Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath\n- Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve\n- Take the highest-value research workflows from experiments to reliable, production-grade jobs\n- Improve the throughput, cost, and reliability of large-scale inference and scoring workloads\n- 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\n\n### Minimum qualifications:\n- Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python\n- Experience building and operating data-intensive or distributed systems in production\n- Experience building tooling or infrastructure that other engineers or researchers use as a dependency\n- Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems\n- Ability to debug performance and correctness problems across an unfamiliar stack\n- Strong written and verbal communication skills, and a collaborative approach to technical decisions\n\n### Preferred qualifications:\n- Experience with high-performance, large-scale machine learning systems\n- Familiarity with language modeling and transformers, including working with model internals\n- Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization\n- Experience building experiment tracking, caching layers, or evaluation harnesses for research teams\n- Experience with probes, interpretability, or classifier development\n- Interest in the misuse risks of AI systems and a desire to work on mitigating them