Senior / Staff AI Engineer

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative 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. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. 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 Collaborate with research, product, and engineering teams to turn experimental AI workflows into reliable, reusable platform capabilities What You'll Bring 5+ years building production software systems, with experience in AI/ML infrastructure, ML platforms, distributed systems, data platforms, or backend infrastructure Experience operating non-deterministic AI or ML workloads in production or at significant scale — you are comfortable reasoning about behavior across models, tools, environments, and multi-step execution Experience building infrastructure for experimentation, evaluation, model development, synthetic data, agentic workflows, training, inference, or production ML systems Strong prof