Snowflake
Engineering
SeniorAppliedAIEngineer
“Senior Applied AI Engineer at Snowflake. Skills: Applied AI, LLM productionization, Customer engagements, Technical leadership. Lead Customer Programs. Own AI Quality”
What You'll Achieve.
Deliver results; Achieve full potential; Customer outcomes; Raise the bar on accuracy, faithfulness, and safety; Scale Snowflake's impact across customers
Industry & Context.
Solve problems; Accelerate impact; Translate ambiguous customer goals into measurable quality metrics; Translate ambiguous business objectives into robust, scalable, and performant solutions; Structure and execute on complex, open-ended problems
Spend at least 25% of your time onsite, Willingness to travel
What They're Looking For.
Must Have
Demonstrated experience leading technical projects or teams, setting technical direction, reviewing others' work, driving delivery to completion, Proven experience building and productionizing applications using LLMs, technologies like RAG, agentic workflows, Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems, using evals to systematically improve quality over time, Excellent problem-solving and communication skills, articulate complex technical concepts to both technical and executive stakeholders, Comfort with ambiguity, ability to independently structure and execute on complex, open-ended problems, 5+ years of professional software engineering experience, Experience in a customer-facing technical role, Willingness to travel
Nice to Have
Experience building eval sets from production traces and synthetic data, running structured experimentation (A tests, ablations, offline evals) to compare prompts, models, or agent architectures, Familiarity with eval and observability tooling (e. g. , Braintrust, LangSmith, Arize, Weave, Promptfoo), experience building custom eval harnesses, Experience with failure-mode analysis on agent or RAG systems, categorizing errors (hallucination, retrieval miss, planning failure, tool misuse), driving each down with targeted evals, Hands-on experience with the MLOps lifecycle, model deployment, monitoring, evaluation in a cloud environment (AWS, Azure, or GCP), Familiarity with core data science libraries and tools (e. g. , pandas, numpy, Snowpark), Startup experience or experience in a high-growth, fast-paced environment
What You'll Do.
Lead Customer Programs
Grow and Mentor Engineers
Deliver with Velocity
Productionize AI at Scale
Be a Strategic Technical Advisor
Collaborate to Innovate
Drive Compounding Outcomes
How You'll Work.
Team & Collaboration
Collaborate cross-functionally with Product and Engineering teams; Work closely with Snowflake's most strategic customers; Serve as senior technical voice at intersection of product, engineering, and customer success
Communication Scope
Articulate complex technical concepts to both technical and executive stakeholders; Articulate complex technical concepts to technical and executive stakeholders
Process & Methodology
Own the full lifecycle of complex, multi-engineer AI engagements, Scoping, Architecture, Deployment, Monitoring, Handoff, Accountable for delivery quality and customer outcomes
Applying for this Senior Applied AI Engineer role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Ashby
- Ashby is a fast modern ATS — most applications take under 3 minutes.
- The resume parser is strong; verify parsed experience dates and job titles.
- Custom screening questions are often scored algorithmically — answer completely.
- Location field affects geo-based screening; use your actual metro area.
ANONYMOUS · UNFILTERED
What do employees actually say about Snowflake?
Real rants from real employees. Read before you apply.