# Senior / Staff Software Engineer — AI Platform

Source: https://realanalystjobs.com/job/senior-staff-software-engineer-ai-platform-snorkel-ai-e71d748a
Senior / Staff Software Engineer — AI Platform at Snorkel Ai, an on-site role based in New York, New York, USA. Experience level: 5+ Years. Posted on 8 September 2026, read from Snorkel Ai's own careers page (Greenhouse), which RealAnalystJobs re-checks daily and removes the role when it closes. Snorkel Ai has approved H-1B petitions on USCIS record (employer-wide, not a promise for this role).

Updated 15 November 2026.

## Key facts

- Company: [Snorkel Ai](https://realanalystjobs.com/companies/snorkel-ai)
- Location: New York, New York, USA
- Work mode: On-site
- Posted: Sep 8, 2026
- Employment type: Full time
- Experience level: 5+ Years
- Source: Snorkel Ai's careers page (Greenhouse), re-checked daily

Skills: [Python](https://realanalystjobs.com/skills/python), AWS, Airflow

## Visa sponsorship

The posting does not say.

Snorkel Ai had H-1B petitions approved for 25 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/snorkel-ai#h1b-record)

## About this role

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 Software Engineers at Senior / Staff levels 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 proficiency in Python and experience building production-quality APIs, services, and developer tooling

• Strong background in distributed systems and cloud platforms (AWS preferred), including compute orchestration, storage, networking, isolation, and failure handling

• Experience with workflow or distributed execution frameworks such as Prefect, Airflow, Dagster, Ray, Kubernetes, or similar systems

• Strong understanding of production system fundamentals - observability, telemetry, reliability, performance, debugging, incident response, and cost management

• Ability to reason about AI system quality beyond traditional service metrics, including evaluation design, experiment reproducibility, behavioral regressions, and model or agent variability

• Track record of leading complex engineering initiatives, influencing stakeholders, and delivering measurable impact

• Ability to work in a fast-paced environment with strong technical communication skills

• Fluency with modern AI and developer tooling and a willingness to rapidly evaluate and adopt new models, frameworks, infrastructure, and techniques as the ecosystem evolves

Nice to Have

• Experience building or operating LLM or agent infrastructure, including model gateways, agent runtimes, to

Description from Snorkel Ai's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you.

## How to apply

Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/senior-staff-software-engineer-ai-platform-snorkel-ai-e71d748a) and use Apply now there to reach the employer's application. The listing is re-checked against Snorkel Ai's careers page daily and removed when it closes.

## Snorkel Ai

2 open analyst roles on Real Analyst Jobs. [More roles at Snorkel Ai](https://realanalystjobs.com/companies/snorkel-ai)

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## Sources and links

- [This role on Real Analyst Jobs](https://realanalystjobs.com/job/senior-staff-software-engineer-ai-platform-snorkel-ai-e71d748a)
- [Snorkel Ai on Real Analyst Jobs](https://realanalystjobs.com/companies/snorkel-ai)
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