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Snowflake

Runs a cloud platform for business data and AI

Senior Technical Program Manager - ML/AI

Program ManagementConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • At least 5 years of technical program management experience at a cloud or AI-native technology company, with a track record of owning cross-team programs end-to-end.
  • Demonstrated experience with ML/AI programs and understanding of the machine learning development lifecycle, including training, evaluation, deployment, and monitoring.
  • Technical depth to engage meaningfully with machine learning engineers and research scientists on model architecture, inference infrastructure, evaluation metrics, and deployment tradeoffs.
  • Ability to structure execution around experimentation cycles and adapt plans as signals emerge in ambiguous ML/AI programs.
  • Experience managing programs spanning at least four engineering teams with competing roadmaps and shared dependencies.
  • Ability to synthesize complex technical and research concepts into clear, actionable narratives for leadership, product, and cross-functional audiences.
  • Program management fundamentals including dependency management, risk frameworks, milestone governance, stakeholder alignment, and escalation discipline.

Responsibilities

  • Own and drive complex, cross-team ML/AI programs end-to-end, from scoping and planning through execution and launch, across model infrastructure, inference pipelines, AI functions, Cortex AI services, and AI-powered application features.
  • Partner with machine learning engineers, research scientists, product managers, and engineering leaders to define program roadmaps, surface dependencies, and build execution plans that account for the uncertainty and iteration cycles inherent to ML/AI development.
  • Coordinate cross-organization launches for AI features spanning ML infrastructure, application surfaces such as Streamlit, Notebooks, and Cortex Code, and the core platform, ensuring teams ship cohesively and on time.
  • Adapt program structures to handle model experimentation cycles, evaluation gates, accuracy and latency tradeoffs, and the non-linear nature of ML research-to-production timelines.
  • Establish program governance and reporting cadences; track milestones, risks, decisions, and engineering health, and proactively escalate when programs need intervention.
  • Drive cross-functional alignment among ML/AI engineering, product, design, infrastructure, security, and legal/compliance teams on AI feature launches, model deployment, and responsible AI practices.
  • Identify and resolve systemic execution bottlenecks by designing lightweight processes and tooling-backed workflows that improve engineering velocity without adding overhead.
  • Own AI launch readiness by coordinating model evaluation sign-off, performance benchmarking, safety review, and documentation across engineering, product, and trust/safety teams.
  • Represent ML/AI programs to senior leadership with clear, data-backed status updates and strategic recommendations connecting program execution to business outcomes.

Desired Qualifications

  • Experience with large language model inference, fine-tuning, retrieval-augmented generation architectures, or AI function platforms at production scale.
  • Background in model-serving infrastructure, GPU/accelerator resource management, or ML platform engineering.
  • Familiarity with responsible AI practices, including model evaluation, bias detection, safety review, and compliance workflows.
  • Experience with Snowflake's architecture or data cloud ecosystem.
  • Working knowledge of ML observability, including monitoring model performance, drift detection, and production quality metrics.
  • Background in launching customer-facing AI features where accuracy, latency, and cost are critical tradeoffs.

About the Company

Snowflake

Runs a cloud platform for business data and AI

Snowflake runs a cloud platform where businesses store and analyze data, build AI tools, and share information under common access controls. Its platform separates stored data from computing resources, letting teams run different workloads without maintaining database servers. Analysts query business records with SQL, while developers use Python and other languages to build applications and train models against the same data; Cortex connects AI models to company records so teams build assistants and automate analysis. Customers pay for storage and computing usage across Amazon, Microsoft, and Google infrastructure. Snowflake was founded in 2012 and has its headquarters in Menlo Park, California.

Company size
10,001+
Company stage
IPO
Headquarters
Menlo Park, CA
Founded
2012

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