Research Scientist, Snorkel AI
RL Training
$200-275k
The AI data research lab
Be an early applicant
The AI data research lab
501-1000 employees
Be an early applicant
$200-275k
501-1000 employees
Company mission
To empower everyone to solve their most impactful problems through data-centric AI.
Role
Who you are
- Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work
- Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure
- Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL
- Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows
- Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus
- Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints
- Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered
What the job involves
- We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models
- This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering
- You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data
- Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models
- Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks
- Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries
- Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products
- Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach
- Contribute to Snorkel's research publications and internal knowledge base in RL and model training
Our take
Demand for AI is increasing in almost every industry, but the machine learning that fuels it is tedious to set up. In order to train machine learning algorithms, training data must be labelled manually, and this contributes to expenses in time, money and resources when integrating AI within a business.
Snorkel AI develops programmatic approaches to data labelling in order to automate the setup process of machine learning and decrease the required timeframe for a business to begin providing AI services. In addition to these automated data categorization services, the company also integrates data training, management, and analysis into a unified AI-deployment platform.
The complex computer science behind AI setup has resulted in many businesses without computer science backgrounds being left behind, and Snorkel AI has set this as their target audience. And recently, the company raised some serious funding which it plans to use to expand the company;'s engineering, research, and go-to-market efforts.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 4 rounds)
May 2025
$100m
SERIES D
Aug 2021
$85m
SERIES C
Total funding: $235m
Company benefits
- Health: Snorkelers and their dependents are covered by comprehensive medical, dental, and vision plans.
- Wellness: All Snorkelers get a yearly wellness stipend to use on anything related to health and well-being.
- PTO and rest days: We provide unlimited personal time off so you can find your work-life balance. Plus, two company-wide rest days per quarter in addition to company holidays.
- One global team: From our headquarters in Redwood City, CA, to regional offices in San Francisco and New York, we work together as one global team.
- Events: Get to know your fellow Snorkelers outside of work! We host regular events and learning opportunities for our teams across our offices and virtually to connect, have fun, and grow.
- Family leave: Snorkel offers generous parental leave for both birthing and non-birthing parents based on eligibility.
Company HQ
Centennial, Redwood City, CA
Leadership
Alex Ratner
(Co-Founder & CEO)Studied at Harvard and received a PhD from Stanford. They are also an Assistant Professor at the University of Washington.
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