About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

About the Role

Coding Fellows create high-quality technical content used to train and evaluate AI systems. Depending on the project and each Fellow’s strengths, their work may include writing and reviewing code, opening pull requests, creating annotations or documentation, improving others’ work, and coaching peers.

We are looking for a leader to directly manage a portfolio of Fellows who may contribute across multiple projects over time. Project delivery teams own day-to-day tasks, priorities, and outcomes; this role owns the people-management layer across projects, including expectations, performance, support, development, administration, and a fair, consolidated view of each Fellow’s contribution.

This is a people-first role. You should have enough technical depth to understand and discuss Fellow work, but you will not be the day-to-day technical lead or approver for every project.

What you will do

Lead and support Coding Fellows

Build trusted relationships through regular one-on-ones and ongoing communication. Understand each Fellow’s strengths, goals, challenges, and support needs; set clear expectations; and partner with People Operations and project leaders on onboarding and administrative matters.

Own cross-project performance management

Set expectations for quality, reliability, collaboration, communication, flexibility, learning, and professional conduct. Gather and synthesize feedback across projects, accounting for assignment complexity, project context, ramp time, and changing responsibilities. Recognize strong performance, provide candid feedback, and address concerns early through focused improvement plans.

Maintain technical credibility and quality

Review representative work, including code, pull requests, reviews, annotations, documentation, and commentary, to understand quality and development needs. Partner with delivery and quality leads to identify recurring issues and distinguish individual performance concerns from unclear requirements, insufficient context, or process gaps.

Partner with project delivery teams

Create effective feedback loops with project managers, technical leads, reviewers, and other delivery partners. Establish clear expectations when Fellows join projects, preserve context during transitions, surface assignment mismatches or blockers, and resolve conflicts with fairness and discretion.

Coach growth and strengthen the program

Hold regular career conversations and help Fellows grow as individual contributors, reviewers, quality specialists, coaches, or mentors. Use contextualized metrics to monitor quality, throughput, reliability, team health, and retention; improve feedback and calibration processes; and contribute to hiring and workforce planning as the program scales.

What this role owns—and does not own

You own: Fellow support, people administration, performance expectations, consolidated feedback, performance reviews, coaching, development, and long-term success.

Project delivery teams own: day-to-day task assignment, project-specific priorities, delivery plans, immediate deliverable acceptance, and project execution.

You will work closely with delivery teams, but you will not run every stand-up, manage every task, or assess performance using raw activity metrics alone.

What we are looking for

3+ years of experience in software engineering, technical leadership, or a related environment, including direct management of programmers or software engineers.
A computer-science degree is not required. Experience with AI/ML development, model evaluation, annotation, data quality, or technical-content production is helpful.