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Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Advance AI data and agentic environment creation by designing scalable pipelines combining automated generation, real-world data, and expert judgment. Run experiments, evaluate data and model performance, collaborate with engineering, product, customers, and domain experts, and translate research prototypes into production workflows. The role also involves developing expert-authoring workflows, monitoring frontier research, and representing findings through publications, talks, blogs, and customer engagements.

Drafts, reviews, and negotiates customer, vendor, and other commercial agreements. Partners with business teams, manages high-volume contract workflows, improves templates and playbooks, and supports contract lifecycle processes and legal projects. The role requires strong commercial judgment, communication, negotiation skills, and the ability to balance legal risk with business objectives in a fast-growing technology company.

Own Snorkel AI’s communications strategy across press, podcasts, awards, speaking opportunities, and media coverage. Develop compelling stories from research, benchmarks, customer results, and executive perspectives. Write pitches, bylines, releases, and supporting content while partnering with Research, Product Marketing, and Content teams. Build relationships with technology and business media, podcasts, and influencers, manage awards and conference submissions, and track communications performance.

Own Snorkel’s Agentic Data and Reinforcement Learning Environments product roadmap. Define data product strategy, shape new product areas, and collaborate with research, academic partners, go-to-market, and operations teams. The role requires deep AI/ML and LLM post-training knowledge, experience building data products and technical roadmaps, customer discovery with technical buyers, quantitative market and unit-economic analysis, and strong influence across research-oriented stakeholders.

Leads Snorkel AI’s GTM systems strategy, Salesforce architecture, data governance, automation, and AI transformation. Manages a technical systems team, owns the GTM technology roadmap and vendor relationships, improves lead-to-cash processes, and partners across Sales, Delivery, Marketing, Finance, Legal, and Customer Success. The role drives scalable workflows, reliable revenue data, AI-enabled pipeline and forecasting solutions, and technology adoption across the organization.

Build infrastructure for large-scale AI and agentic workloads, including synthetic data generation, evaluation, orchestration, distributed compute, simulation environments, LLM routing, observability, and developer tooling. The role requires operating nondeterministic AI systems in production, improving reproducibility and reliability, and leading complex engineering initiatives across research, product, and engineering teams.

Build and deliver computer-use agent environments, datasets, evaluators, verifiers, and production solutions for AI labs and enterprise customers. Lead technical engagements from discovery through implementation and delivery, diagnose agent failures, run experiments, and improve reliability and performance. Partner with customers and internal engineering, research, and product teams while creating reusable frameworks, tooling, standards, and technical best practices. Provide technical leadership and communicate tradeoffs and recommendations to stakeholders.

Lead and scale Snorkel AI's technical recruiting function: build and manage a team of recruiters and sourcers, own hiring strategy and operating model, partner with technical leaders, design sourcing and pipeline strategies, use data to optimize hiring metrics, coach interviewers, and represent the company externally to attract top technical and AI talent.

The Senior Product Manager will lead development for AI data systems, ensuring product quality, improving workflows, and collaborating with cross-functional teams to deliver scalable solutions.