Staff Applied AI Engineer

Requirements

• Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion.
• Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to both technical and executive stakeholders.
• Comfort with ambiguity and the ability to independently structure and execute on complex, open-ended problems.
• Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows.
• 8+ years of professional software engineering experience.

Nice to Haves

• Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment (AWS, Azure, or GCP).
• Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark).
• Experience in a customer-facing technical role.
• Experience defining and executing AI project roadmaps across multiple concurrent engagements or customers.
• Startup experience or experience in a high-growth, fast-paced environment.

What You'll Be Doing

• Lead Customer Programs: Own the full lifecycle of complex, multi-engineer AI engagements — from scoping and architecture through deployment, monitoring, and handoff. Be accountable for delivery quality and customer outcomes across your portfolio of strategic accounts.
• Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship to a team of 2–4 Applied AI Engineers. Review designs and code, unblock teammates, and actively develop their skills and careers.
• Deliver with Velocity: Remain a hands-on contributor — designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions alongside your team. Translate ambiguous business objectives into robust, scalable, and performant solutions.
• Productionize AI at Scale: Own the full implementation lifecycle for AI solutions, from developing prototypes to deploying, monitoring, and optimizing them in secure, large-scale production environments. Define evaluation frameworks, safety guardrails, observability, and human-review workflows that keep AI applications reliable, measurable, and trustworthy in production.
• Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer data science and engineering leadership. Set the standard for how Snowflake AI is deployed and articulate complex technical concepts to both technical and executive stakeholders.
• Collaborate to Innovate: Work cross-functionally with Snowflake's Product and Engineering teams, bringing real-world patterns and feedback from the field to directly shape the future of Snowflake's AI platform.
• Drive Compounding Outcomes: Identify and champion improvements to team practices. Identify recurring deployment patterns and turn them into reusable assets, reference architectures, evaluation harnesses, and product feedback that scale Snowflake’s impact across customers.
• Have the opportunity to travel: Spend time onsite, working closely with Snowflake’s most strategic customers.

Perks and Benefits

• Snowflake is growing fast, and looking for people who share values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.