Senior Manager, Forward Deployed Research

Locations: New York City, NY (Hybrid); Redwood City, CA (Hybrid); San Francisco, CA (Hybrid); US (Remote)
About Snorkel

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

The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the frontier 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. Our Data-as-a-Service (DaaS) organization partners with frontier AI labs to solve some of the hardest data challenges, creating training and evaluation data that power the next generation of models.
About the Role

Snorkel AI is hiring a Senior Manager on the Forward Deployed Research team to own how we show what our data does. You own two related functions: benchmarking the latest frontier models against our data series to expose where they fall short, and tuning customer and open-source models on our data to demonstrate the lift it produces. Both become the evidence behind our data pitch and a signal for what to build next.

This is a player-coach role. You own the system, the standards, and the output: the methodology, the quality bar, and the intelligence we produce, while hiring and developing a small team of engineers and researchers. You define the tooling and automation the function needs and partner with Engineering to build it. You stay hands-on in the technical work and grow the function as demand climbs.

You partner across GTM, where this work opens and advances deals, with Research as a research partner, and with Engineering to build the underlying tooling. The right person is a strong engineer with real evaluation depth, can hold their own on frontier AI, and wants to own a function and grow a team.
Main Responsibilities

• Own the system for measuring what our data does: define how we benchmark and tune models on our data, and the tooling and automation the function needs, partnering with Engineering to build it
• Recruit, hire, and develop a small team of engineers and researchers; a player-coach role, hands-on technical work plus team leadership
• Own the methodology and playbook for benchmarking and tuning: the model panels, the metrics we report, the tuning setups, and the quality bar, so results are consistent, repeatable, and defensible across accounts and data series
• Turn benchmark results into gap intelligence: clear analyses of where models fall short that serve as the evidence behind our data pitch and a primary input to what we build next
• Tune customer and open-source models on our data to demonstrate the lift it produces, and turn that into presales material and intelligence
• Partner cross-functionally with the GTM team where this work opens and advances deals, with Research as a research partner, and with Engineering, who build the underlying platform and automation you direct
• Serve as the technical authority on evaluation: escalation point for complex or high-stakes benchmarking, and the person ensuring our published pass rates and model comparisons are rigorous and credible

Preferred Qualifications

• 8+ years in applied ML, model evaluation, or research-intensive engineering, with a track record of building technical systems
• Strong software engineering skills, with experience building data or evaluation pipelines, automation, and tooling that scale
• Deep understanding of model evaluation and benchmarking: designing evaluations, selecting model panels and metrics, and producing rigorous, defensible results
• Strong fluency in frontier AI concepts including LLMs, evaluation methodologies, post-training techniques (RLHF, DPO, RLAIF), and domain areas such as coding agents, reasoning, multimodal models, or RL environments
• Experience leading, hiring, and developing technical talent; comfortable as a player-coach who stays hands-on while growing a team
• Ability to translate technical results into clear insights for technical and go-to-market audiences, and to partner effectively across research, engineering, and GTM
• Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration
• M.S. in Computer Science, Machine Learning, or related field

Compensation

The base salary range for this position is $185,000-$321,600, depending on location, experience, and qualifications. This role is also eligible for variable compensation, and stock grants.

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

Salary range(s) for this role

$185,000-$321,600 USD