Senior/Staff Forward Deployed Engineer, Applied AI

US-CA-Menlo Park · 7+ years experience · Full-time · Hybrid · Posted Sep 24, 2026

At a glance

As a Senior/Staff Forward Deployed Engineer, Applied AI on the Cortex AI team at Snowflake, you will act as a hands-on technical leader and trusted partner to strategic customers.
You will own the end-to-end delivery of enterprise AI programs, leading a team of engineers while remaining deeply technical.
You will set technical direction for customer engagements, mentor team members, and serve as the senior technical voice at the intersection of product, engineering, and customer success.

Lead Customer Programs: Own the full lifecycle of complex, multi-engineer AI engagements from scoping and architecture through deployment, monitoring, and handoff.
Own AI Quality: Define engagement standards, translate ambiguous customer goals into measurable quality metrics and evaluation frameworks, and run systematic eval loops.
Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship to Applied AI Engineers, review designs and code, and develop their careers.
Deliver with Velocity: Remain a hands-on contributor designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions.
Productionize AI at Scale: Own the full implementation lifecycle for AI solutions from prototype through deployment, monitoring, and optimization in secure production environments.
Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer data science and engineering leadership and articulate complex technical concepts.
Collaborate to Innovate: Work cross-functionally with Product and Engineering teams, bringing real-world patterns and feedback from the field to shape Snowflake's AI platform.
Drive Compounding Outcomes: Identify recurring deployment patterns and turn them into reusable assets like reference architectures and evaluation harnesses.
Spend at least 25% of your time onsite, working closely with Snowflake's most strategic customers.

Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion.
Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows.
Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems, and using evals to systematically improve quality over time.
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.
7+ years of professional software engineering experience.
Experience in a customer-facing technical role.
Willingness to travel.

Experience building eval sets from production traces and synthetic data, and running structured experimentation (A/B tests, ablations, offline evals).
Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or experience building custom eval harnesses.
Experience with failure-mode analysis on agent or RAG systems.
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).
Startup experience or experience in a high-growth, fast-paced environment.

Spend at least 25% of your time onsite working closely with strategic customers.

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