Senior Staff Software Engineer (AI/ML)

Location: San Francisco, CA (Hybrid)

Salary: $208,000–$315,000 USD per year

Company

Snorkel AI develops technology that helps enterprises transform expert knowledge and proprietary data into specialized, production-ready AI systems.

What you will do

• Develop efficient agentic evaluations using adaptive sampling, early stopping, model cascades, caching, and cost-aware gating.
• Build AI model-routing systems with fallbacks, monitoring, and cost attribution.
• Fine-tune and serve open-weight models using parameter-efficient methods such as LoRA.
• Build predictive models for estimating task difficulty before frontier-model rollouts.
• Create golden datasets and measure the accuracy and calibration of LLM-as-judge systems.
• Turn research prototypes into reusable, configurable components for engineers and researchers.

Requirements

• 5+ years of experience building production ML or software systems, with end-to-end ownership from prototype to production.
• Hands-on experience running LLM or ML workloads in production and reasoning about non-deterministic systems.
• Strong knowledge of statistics and experimentation, including experiment design, hypothesis testing, sampling, and confidence intervals.
• Strong Python and software engineering fundamentals, including testing, code review, and system design.
• Experience designing evaluations and interpreting results rigorously.
• Clear communication with researchers, engineers, and business partners.

Nice to have

• Experience fine-tuning and serving open-weight models.
• Experience building LLM evaluation platforms, experimentation platforms, model gateways, or routing systems.
• Experience with agentic workloads, benchmarks, or reinforcement learning environments.
• Research-to-production experience demonstrated through publications, open-source work, or shipped research-driven features.
• MS or PhD in Computer Science, Machine Learning, Statistics, or a related field.

Culture & Benefits

• Work on frontier AI measurement and evaluation problems.
• Help define ML engineering practices and grow an emerging ML and Research Engineering team.
• Meaningful influence over technical priorities and strategic decisions.
• Opportunities for technical growth, leadership development, and cross-functional learning.
• Join a well-funded company scaling its engineering and research teams.