THE ROLE

As an Applied Research Engineer at Snorkel AI, you will own the infrastructure that powers our model training and evaluation work. This is a hands-on role where you will build and operate GPU cluster infrastructure, training pipelines, and the tooling that allows our research and engineering teams to run experiments reliably and at scale. You will work closely with research scientists and engineers, translating training requirements into robust, reproducible systems—and proactively removing infrastructure blockers before they slow down the work that matters most.

Snorkel AI operates in a fast-paced, high-impact environment. We are looking for someone who takes pride in operational excellence, loves solving complex distributed systems problems, and thrives when given real ownership.

Location: Redwood City or San Francisco — OR REMOTE

MAIN RESPONSIBILITIES

• Set up and manage GPU cluster infrastructure on major cloud providers (e.g., AWS HyperPod) for distributed model training, including networking, provisioning, and cost tracking.
• Build and operate job orchestration and scheduling systems (e.g., Kubernetes, Slurm, or cloud-native equivalents) to reliably launch and manage training, rollout, and evaluation jobs across multi-node clusters.
• Integrate and maintain ML training frameworks and post-training pipelines, ensuring they run stably and reproducibly at scale.
• Set up and maintain experiment tracking, dataset versioning, and model artifact management to support fast iteration.
• Monitor and optimize cluster health, inter-node communication, and resource utilization; implement fault tolerance and auto-recovery so long-running jobs survive node failures.
• Work closely with research scientists and ML engineers to understand requirements, unblock experiments, and evolve infrastructure as our training workloads needs change.

PREFERRED QUALIFICATIONS

• Hands-on experience managing GPU clusters on major cloud providers, including provisioning, network configuration, and cost management.
• Experience with distributed compute orchestration tools such as Kubernetes, Slurm, or equivalent cluster management systems.
• Working knowledge of distributed training concepts: parallelism strategies, memory optimization techniques, and inter-node communication.
• Experience with setting up, managing, and integrating ML experiment tracking and data/model versioning tools..
• Strong Python proficiency and solid software engineering fundamentals such as version control, modular design, and automation.
• Ability to work in a fast-moving, iterative environment and take end-to-end ownership of ambiguous infrastructure problems.
• Hands-on experience with post-training workflows such as supervised fine-tuning (SFT) or reinforcement learning (RLHF, GRPO, or similar) is a strong plus, but not required.

This role is a great fit for engineers who love building reliable systems close to the frontier of AI research. We welcome applicants from a wide range of backgrounds—whether your experience comes from industry, research labs, or direct hands-on work with distributed infrastructure at scale.

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 THE ROLE 

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. 

MAIN RESPONSIBILITIES 

• 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.

PREFERRED QUALIFICATIONS 

• 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. 

ABOUT THE ROLE 

We're looking for a Staff or Senior Research Scientist to collaborate with partners and lead the development of the next frontier benchmarks and datasets. This is a highly visible, customer-facing role at the intersection of research, company strategy, and go-to-market. You'll design datasets taking into account frontier model performance and work with our academic partners, and then partner with delivery, product and go-to-market to scale out production. You will also  serve as a credible technical partner for our customers, prospects, and drive results that impact the broader research community. 

This role reports directly to the Head of Research and is ideal for someone who is energized by cross-functional work and wants to understand how startups operate across research, data operations, and commercial teams. 

MAIN RESPONSIBILITIES 

• Design state of the art datasets that drive frontier model training and evaluation based on current model performance and academic partnerships 
• Translate benchmark insights into clear, compelling narratives that articulate the ROI of expert-curated data for customer-facing presentations, technical reports, and go-to-market materials.
• Work cross-functionally with data operations, product, engineering, and strategy to surface research findings that inform the company roadmap. 
• Stay at the frontier of LLM evaluation research and bring best practices into Snorkel's workflows
• Represent Snorkel's research externally through publications, blog posts, conference talks, and customer engagements that advance the conversation around data-centric AI

PREFERRED QUALIFICATIONS 

• Strong research background in AI/ML evaluation, NLP, or related fields, with a track record of rigorous experimental design — especially around measuring the impact of training and evaluation data on model behavior. 
• Exceptional communication skills — able to present complex technical findings clearly to both technical and non-technical audiences 
• Comfort operating in a fast-moving, cross-functional environment with ambiguous problem spaces 
• Genuine interest in GTM strategy, startup dynamics, and the commercial side of AI data services. 
• Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.

Snorkel AI came out of the Stanford AI lab and is built on the principle that data development is the core of AI. For years, we have pioneered data-centric methods that power the world’s most sophisticated AI implementations from top enterprises to frontier AI labs. We are looking for a Senior Manager of Research to lead a team focused on data-centric research.

About the Role

As a manager on the Research team, you will lead a team that focuses on data-centric research that drives the datasets and associated methods that the next generation of models will be trained and evaluated on. From collaborating with researchers at frontier labs to academics at Stanford, Berkeley, UW, you and your team will bridge the gap between cutting edge research and real-world AI development. 

Main Responsibilities

• Team Building & Leadership: Recruit for and lead a team of applied researchers, setting the team’s culture and setting a high standard for quality and velocity
• Frontier Data Research: Collaborate with academic and industry-leading AI labs to define the data-centric techniques that will help power the next generation of foundation models.
• Strategic Roadmapping: Anticipate the next bottlenecks in the AI data (e.g., automated RLHF, agentic evaluation, or specialized model alignment) and pivot the team to develop the programmatic solutions that address them
• Operational Excellence: Partner with the Go-to-Market, Operations and Product teams to enhance current state and drive the roadmap for what the company will focus on next

What We’re Looking For

• 5+ years of experience in applied AI, research or machine learning roles, with at least 4+ years of experience managing technical teams
• A player coach who is willing to be hands-on with coding, research, customer-facing engagements
• A proven track record of thriving in fast-paced, ambiguous environments, comfortable managing competing priorities across cross-functional stakeholders in Product, Engineering, and GTM
• Enjoys solving real-world problems in the data space and bridging research and production
• Strong, practical experience working with LLMs, agentic workflow
• Bonus: Experience with data annotation workflows and/or building internal tooling for them

Location: San Francisco, CA or Remote
Reports to: Head of Research (Co-founder)