# Staff Forward Deployed Engineer, Applied AI

**Company**: Snowflake
**Location**: Menlo Park, California
**Experience**: staff
**Job type**: full-time
**Salary**: $200K - $287.5K
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.ashbyhq.com/snowflake/fe41fd7e-56ad-4bdc-b13f-20adb2542171?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_fa836a28-7ea

## Description

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work.

As a Staff Forward Deployed Engineer, Applied AI on our Cortex AI team, you will be a hands-on technical leader and trusted partner to our most strategic customers. You will own the end-to-end delivery of enterprise AI programs, leading a team of 2–4 engineers while staying deeply technical yourself.

**Responsibilities**

- 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 what "good" means for each engagement. Translate ambiguous customer goals into measurable quality metrics, evaluation frameworks, and golden datasets – then run systematic eval loops to hill-climb on agent quality, catch regressions before customers do, and continuously raise the bar on accuracy, faithfulness, and safety.

- Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship to a team of 2–6 Applied AI Engineers.

- Deliver with Velocity: Remain a hands-on contributor – designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions alongside your team.

- Productionize AI at Scale: Own the full implementation lifecycle for AI solutions, from prototype through deployment, monitoring, and optimization in secure, large-scale production environments.

- Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer data science and engineering leadership.

- 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 recurring deployment patterns and turn them into reusable assets – reference architectures, evaluation harnesses, and product feedback that scale Snowflake's impact across customers.

**Requirements**

- 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.

**Preferred Qualifications**

- Experience building eval sets from production traces and synthetic data, and running structured experimentation (A/B tests, ablations, offline evals) to compare prompts, models, or agent architectures.

- 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 – categorizing errors (hallucination, retrieval miss, planning failure, tool misuse) and driving each down with targeted evals.

- 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.

## Skills

### Required
- LLMs
- RAG
- agentic workflows
- quality metrics
- evaluation frameworks
- problem-solving
- communication skills

### Nice to have
- eval sets
- synthetic data
- structured experimentation
- eval and observability tooling
- failure-mode analysis
- MLOps lifecycle
- core data science libraries

---

Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/snowflake/fe41fd7e-56ad-4bdc-b13f-20adb2542171?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)