Senior / Staff AI Engineer

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 Team

Snorkel's AI Platform organization builds the infrastructure and systems that power AI development at scale - synthetic data generation, evaluation, agentic workflows, simulation environments, LLM infrastructure, and distributed compute. Our platform enables engineering and research teams to rapidly experiment with models and agents, measure their behavior, and turn successful experiments into reliable production systems.

We're a small team operating at the intersection of distributed systems and applied AI, and we're in the middle of a foundational shift toward agent-first workflows where models interact with tools, environments, data, and other agents over long-running trajectories. The systems we build need to make these inherently non-deterministic workloads observable, reproducible, measurable, and scalable. You will help define how we do that.

About The Role

We're looking for AI Engineers who combine strong software and distributed systems fundamentals with experience operating AI systems in production. You'll build the infrastructure that lets teams create, experiment with, evaluate, and operate LLM and agentic workloads at significant scale - from synthetic data and evaluation pipelines to simulation environments, orchestration systems, and LLM infrastructure.

You'll work on systems where correctness is not defined by a single deterministic output. Instead, you'll build the infrastructure needed to understand behavior across models, prompts, tools, environments, and multi-step trajectories, and to continuously improve those systems through experimentation and evaluation.

We are looking to grow our team of AI Engineers, and are hiring at multiple levels.

What You'll Do

Design and build infrastructure for running large-scale agentic workloads, including multi-step agents interacting with tools, external services, sandboxes, and simulated environments

Build scalable synthetic data generation and automated labeling systems that allow teams to create, refine, and evaluate high-quality training and evaluation datasets

Design evaluation infrastructure for measuring AI system behavior across models, prompts, tools, environments, and multi-step trajectories - including reproducible experiments, benchmark execution, regression detection, and continuous evaluation

Build orchestration and distributed compute systems for running thousands to millions of AI experiments and simulations reliably across heterogeneous compute environments

Develop infrastructure for agent simulation environments, including environment provisioning, isolation, lifecycle management, and scalable execution

Build and operate LLM infrastructure for routing, rate limiting, retries, caching, provider failover, cost attribution, and efficient execution across multiple model providers

Instrument agent and model workloads so failures are observable and debuggable - capturing traces, model interactions, tool calls, environment state, evaluation results, latency, reliability, and cost

Design systems that make non-deterministic workloads reproducible and measurable, allowing engineers to compare experiments, diagnose behavioral regressions, and understand why an agent succeeded or failed

Improve the developer experience for AI experimentation by building APIs, SDKs, workflow abstractions, and tooling that make it easy to move workloads from local development to large-scale production execution

Collabor …