Senior Staff Software Engineer (AI / ML)
Snorkel AI
San Francisco, United States Posted October 3, 2026
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Salary Range
$208,000 - $315,000
Job Description
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
Required Skills
How to apply
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