---
title: 'Senior | Staff Software Engineer - AI / ML at Snorkel AI'
canonical: 'https://feeny.ai/job/senior-staff-software-engineer-ai-ml-snorkel-ai-san-francisco-zbmmxra1k7sm'
type: 'job'
last_seen: '2026-09-26'
---

# Senior | Staff Software Engineer - AI / ML at Snorkel AI

- **Company:** Snorkel AI
- **Location:** San Francisco, CA
- **Compensation:** $208k–$315k
- **Work type:** hybrid
- **Posted:** 2026-09-25
- **Last confirmed live:** 2026-09-26
- **Apply:** https://job-boards.greenhouse.io/snorkelai/jobs/6208931004

## Job description

## About Snorkel

At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.

We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

In September 2026 we raised a $350 million Series E at a $3.5 billion valuation, and we are scaling our engineering and research teams to meet demand.

## The role

Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI

You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it.

## What you'll work on

- Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating.
- AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution.
- Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other parameter-efficient methods) where they match frontier quality, and know when they don't.
- Predictive difficulty. Build models that estimate how hard a task is for frontier systems before running a single rollout.
- Measurement for AI data. Build golden datasets, quantify the accuracy and calibration of LLM-as-judge systems, and make quality reproducible across projects.
- Research to production. Turn research prototypes into reusable, configurable components that forward deployed engineers and researchers use on every project.

## What you'll bring

- 5+ years 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 comfort reasoning about non-deterministic systems
- Deep grounding in statistics and experimentation: 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
- A habit of finding high-impact problems before they are assigned, and clear communication with researchers, engineers, and business partners

## Nice to have

- Fine-tuning and serving open-weight models, and judging when a smaller model meets the quality bar
- Building LLM evaluation or experimentation platforms, model gateways, or routing systems
- Experience with agentic workloads, benchmarks, or RL environments
- A record of taking research into production: publications, open-source work, or shipped research-driven features
- MS or PhD in Computer Science, Machine Learning, Statistics, or a related field

## Why join now

- Frontier problems. Measure and shape the tasks designed to challenge the strongest models in the world.
- All AI, no plumbing. Every problem on this team is an open ML or LLM problem.
- Founding impact. Help define what ML engineering means at Snorkel and grow the team that carries it forward.
- Visible results. Your work shows up directly in the speed, quality, and cost of the data frontier AI is built on.

Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.

Salary range(s) for this role $208,000—$315,000 USD

Be Your Best at Snorkel

Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.

Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

## About Snorkel AI

## Company Overview
- **One-liner**: The frontier AI data lab helping teams build the data and environments behind high-performing frontier models and agentic AI.
- **Entity Type**: Private (multiple funding rounds; latest venture round in 2024; total funding $250.2M)
- **Headquarters**: Redwood City, California, United States
- **Founded**: 2019
- **Founders**: Not explicitly listed in provided sources; company originated from the Stanford AI Lab

## Core Business
- **Primary industry**: AI data development, software development, AI/ML research
- **Target customers**: B2B; frontier AI labs, enterprises, government agencies
- **Mission**: “We’re building the data and environments behind the world’s most advanced AI systems.”

## Products & Services
- **Custom Data Development**: Bespoke datasets, evaluation frameworks, and benchmark expansions for specialized AI models and agents. Type: Service / Platform.
- **Specialized Agents**: Agents grounded in expert data, evaluated against task-specific rubrics and programmatic checks. Type: AI solution.
- **Snorkel Data Series**: Curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices. Type: Dataset product.
- **Evaluation Research**: Proprietary frameworks like RIFT (Rubric Failure Mode Taxonomy) and benchmarks such as OSWORLD 2.0 and Terminal-Bench 2.0. Type: Research / tooling.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Annual Revenue $38.6M (LinkedIn estimate) | Total Funding $250.2M (9 rounds)
- **Notable Investors/Partners**: Addition, Greylock, GV (Google Ventures), In-Q-Tel, Lightspeed Venture Partners, funds managed by BlackRock, QBE Ventures
- **Growth Signals**: Headcount grew 62.5% YoY to 801 employees; operates in 15 countries; trusted by leading AI labs and enterprises; 250+ publications at top conferences (NeurIPS, ICML, ICLR, etc.)

## Competitive Advantages
- **Proprietary data development process** based on programmatic data labeling and weak supervision, pioneered at Stanford AI Lab
- **Deep academic roots** with ongoing research collaborations and peer-reviewed publications
- **Focus on frontier AI data quality** (task specification, calibrated review, provenance) rather than volume
- **Embedded delivery model** combining platform, research, and domain expertise

## Strategic Focus
- Building the data and environments for frontier models and agentic AI
- Expanding into enterprise and government deployments
- Advancing research on RLVR (Reinforcement Learning with Verifiable Rewards) in low-data regimes
- Creating evaluation standards for agentic systems (e.g., Terminal-Bench, OSWORLD 2.0)

## Why Work Here
- **Culture**: “Quality first”, “Push the frontier”, “Competitive yet kind” – fast-moving, high bar, no ego
- **Locations**: Primarily San Francisco, Redwood City, and New York City, with remote/hybrid options globally
- **Benefits**: Comprehensive medical coverage, parental leave for birthing and non-birthing parents, flexible PTO, equity refresh grants, wellbeing allowance, company events
- **Engineering culture**: Work with PhDs, subject matter experts, and domain specialists on cutting-edge AI data challenges
- **Open roles**: 45 positions across Research, Engineering, Sales, Operations, Product, etc. (as of latest posting)

## Sources
1. [snorkel.ai/company/](https://snorkel.ai/company/)
2. [snorkel.ai/](https://snorkel.ai/)
3. [snorkel.ai/join-us/](https://snorkel.ai/join-us/)
4. [linkedin.com/company/snorkel-ai](https://www.linkedin.com/company/snorkel-ai)
5. [job-boards.greenhouse.io/snorkelai](https://job-boards.greenhouse.io/snorkelai/jobs/5990959004)

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