Senior AI/ML Engineer
Snorkel AIRedwood City, CA (Hybrid); San Francisco, CA (Hybrid)Posted 16 April 2026
Job Description
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 between 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
We are looking for a Senior AI/ML Engineer to add to our AI Platform team. This is a role for someone who wants to operate at the nexus of distributed systems, LLM infrastructure, synthetic data, and agentic orchestration . Our platform enables an ecosystem of expert contributors to create the datasets and evaluation environments that frontier models rely on to improve. We combine a decade of academic research with production-grade engineering to help customers build differentiated, high-performance AI systems faster than ever before.
About the Role
Architect the core platform that powers synthetic data generation, agentic workflows, RL environments, and scalable LLM operations
Design and evolve the APIs, compute services, and orchestration layers that empower internal and customer-facing applications
Generate and refine synthetic data at massive scale
Create and evaluate agentic systems that reason, act, and improve over time
Provision RL training environments and simulation frameworks for next-generation AI agents
Deploy robust benchmarks, datasets, and automated labeling pipelines to accelerate model development
Run LLMs and multi-agent systems efficiently, reliably, and cost-effectively across cloud and hybrid environments
Work a hybrid schedule with three days per week in our Redwood City HQ or the SF office.
About You
6+ years of experience building customer-facing, cloud-native software systems
Deep experience with AI/ML pipelines, LLM-based systems, or agentic workflows
Experience with distributed computing , large-scale data systems, or orchestration frameworks
Experience at high-growth technology startups
Experience building software products for large enterprise customers
Expertise in Python and cloud platforms (AWS, GCP, or Azure)
Strong understanding of production web-scale systems: monitoring, telemetry, reliability, performance, debugging, and triage
Salary Range
$192,000
$240,000 USD
Be Your Best at Snorkel
Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.
Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or ... (truncated, view full listing at source)
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