Forward Deployed Engineer, Frontier Engineering
SnowflakeCloud Data company
New ZealandSenior
Sequoia Capital
Altimeter Capital
Dragoneer Investment Group
Salesforce Ventures
Redpoint
Sutter Hill Ventures
Data & AINew
About the role
TL;DR
Build and deploy enterprise AI solutions with LLMs and agentic workflows for strategic customers.
- •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.
- •You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact.
- •We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset
- •who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results.
- •At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
- •At Snowflake, we are building a high-impact team to help the world's most innovative companies unlock the power of AI.
- •As a Forward Deployed Engineer on our Frontier Engineering team, you will be a hands-on builder and a key technical partner to our most strategic customers, placing you at the forefront of the enterprise AI revolution.
- •You won't just work with cutting-edge technology
- •you'll deploy it to solve real-world business problems at scale, building production-grade AI systems using Snowpark, Cortex, and our native LLM capabilities.
- •IN THIS ROLE AT SNOWFLAKE, YOU WILL: Build Customer Solutions: Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents.
- •Own the end-to-end lifecycle of your workstreams
- •from prototype to production
- •directly solving customers' most complex business challenges.
- •Own the Quality of What You Ship: Define what "good" means for the systems you build.
- •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.
- •Treat measurement as a first-class part of building, not an afterthought.
- •Deliver with Velocity: Rapidly design, iterate, and ship high-quality code and pipelines.
- •Translate ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL.
- •Productionize AI at Scale: Own the full implementation lifecycle for your solutions
- •from prototype through deployment, monitoring, and optimization in secure, large-scale production environments.
- •Build the safety guardrails, observability, and human-review workflows that keep AI applications reliable and trustworthy, and close the loop from production traces and user feedback back into your evals so quality compounds over time.
- •Be a Technical Partner: Partner directly with customer data science and engineering teams as a hands-on technical resource and trusted advisor on how to best leverage AI for their business challenges.
- •Collaborate to Innovate: Work cross-functionally with Snowflake's Product and Engineering teams, sharing real-world feedback from the field to directly influence the future of Snowflake's AI platform.
- •Have the opportunity to travel: Spend at least 25% of your time onsite, working closely with Snowflake's most strategic customers.
- •WE'RE LOOKING FOR CANDIDATES WHO HAVE: Minimum Qualifications Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience. 3+ years of professional software engineering experience.
- •Willingness to travel.
- •Proven experience building applications using LLMs, especially with technologies like RAG and agentic workflows.
- •Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, and using evals to systematically improve quality.
- •Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to diverse stakeholders.
- •Comfort with ambiguity and a desire to thrive in a fast-paced, ever-changing Generative AI environment.
- •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).
- •Experience in a customer-facing technical role (e.g., solutions architect, sales engineer, or professional services).
- •Startup experience.
- •Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth.
- •We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
- •How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
Required skills
PythonSQLLLMs
Nice-to-have skills
PandasNumPyAWSGoogle CloudAzureLangChainHugging FaceMLflowKubeflowSageMaker
Domain expertise
ai
Tech stack
PythonSQLLLMsPandasNumPyAWSGoogle CloudAzure