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Snorkel AI · Posted 4w ago

Senior Staff Software Engineer (AI / ML)

$208–315K/yrSenior

Job relayed by Stapply Data, its source: you apply there.

Location
🇺🇸 San Francisco, United States
Contract
Full-time
Role
ML engineering
Source
Stapply Data

Stack

The posting

Join Snorkel, a leading company in AI and machine learning. As a Senior Staff Software Engineer, you will be at the forefront of advancing frontier AI data generation and evaluation. You will work on cutting-edge projects, shape the direction of the discipline, and have a significant impact on the company's success. Enjoy a comprehensive benefits package, unlimited PTO, and the opportunity to work with a global team.

- Contribuer à l'accélération, à la réduction des coûts et à l'amélioration de la rigueur des processus d'évaluation des modèles d'IA. - Participer à la génération et à l'évaluation de données de qualité supérieure, en validant les hypothèses avec des données de production réelles. - Transformer les prototypes de recherche en composants réutilisables et configurables utilisés par les ingénieurs et chercheurs déployés.

- Strong Python and software engineering fundamentals, including testing, code review, and system design - Hands-on experience running LLM or ML workloads in production, and comfort reasoning about non-deterministic systems - Deep grounding in statistics and experimentation: experiment design, hypothesis testing, sampling, and confidence intervals - 5+ years building production ML or software systems, with end-to-end ownership from prototype to production - A habit of finding high-impact problems before they are assigned, and clear communication with researchers, engineers, and business partners - Experience designing evaluations and interpreting results rigorously - MS or PhD in Computer Science, Machine Learning, Statistics, or a related field - A record of taking research into production: publications, open-source work, or shipped research-driven features - Experience with agentic workloads, benchmarks, or RL environments - Building LLM evaluation or experimentation platforms, model gateways, or routing systems - Fine-tuning and serving open-weight models, and judging when a smaller model meets the quality bar