Senior Technical Program Manager - ML/AI
US-CA-Menlo Park · US-WA-Bellevue · 5+ years experience · Full-time · Hybrid · Posted Oct 10, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- US-CA-Menlo Park · US-WA-Bellevue
About the role
- At Snowflake, we are powering the era of the agentic enterprise and seek AI-native thinkers energized by the opportunity to reinvent how they work.
- As a Senior Technical Program Manager — ML/AI, you will own and drive complex, cross-team ML/AI programs end-to-end across model infrastructure, inference pipelines, AI functions, Cortex AI services, and AI-powered application features.
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 ML Engineers, Research Scientists, Product Managers, and Engineering Leaders to define program roadmaps, surface dependencies, and build execution plans that account for the unique uncertainty and iteration cycles inherent to ML/AI development
- Coordinate cross-org launches for AI features that span ML infrastructure, application surfaces (Streamlit, Notebooks, Cortex Code), and core platform — ensuring all teams ship cohesively and on time
- Navigate the ambiguity of AI development — adapt program structures to handle model experimentation cycles, evaluation gates, accuracy/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 with clarity and rigor, and proactively escalate to leadership when programs need intervention
- Drive cross-functional alignment between 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 — design lightweight processes and tooling-backed workflows that improve engineering velocity without adding overhead
- Own AI launch readiness — coordinate 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 crisp, data-backed status updates and strategic recommendations that connect program execution to business outcomes
Requirements
- 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 — you understand the ML development lifecycle (training, evaluation, deployment, monitoring) and can manage programs around its unique iteration patterns and uncertainty
- Strong technical depth — ability to engage meaningfully with ML engineers and research scientists on model architecture, inference infrastructure, evaluation metrics, and deployment tradeoffs
- Proven ability to operate in ambiguity — ML/AI programs don't follow predictable timelines; you know how to structure execution around experimentation cycles and adapt plans as signals emerge
- Experience managing programs spanning 4+ engineering teams with competing roadmaps and shared dependencies
- Excellent communication skills — you synthesize complex technical and research concepts into clear, actionable narratives for leadership, product, and cross-functional audiences
- Strong program management fundamentals: dependency management, risk frameworks, milestone governance, stakeholder alignment, and escalation discipline
Preferred qualifications
- Experience with LLM inference, fine-tuning, RAG 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 — model evaluation, bias detection, safety review, and compliance workflows
- Experience with Snowflake's architecture or data cloud ecosystem
- Working knowledge of ML observability — monitoring model performance, drift detection, and production quality metrics
- Background in launching customer-facing AI features where accuracy, latency, and cost are critical tradeoffs
Benefits
- Medical
- Dental
- Vision
- 401(k)
- ESPP
- Generous PTO
- Bonus (15% target)
- Equity in the form of RSUs
Skills
- LLM Inference
- Fine-tuning
- RAG
- GPU/accelerator Resource Management
- ML Platform Engineering
- Responsible AI
- Model Evaluation
- Bias Detection
- Safety Review
- ML Observability
- Drift Detection
