11 days ago
Base Salary
$200k - $288k/yr
Responsibilities
- Own the full lifecycle of complex, multi-engineer customer AI engagements from scoping and architecture through deployment, monitoring, optimization, and handoff.
- Lead teams of 2–6 Applied AI Engineers by setting technical direction, reviewing designs and code, unblocking teammates, and mentoring their development.
- Build and productionize LLM-powered applications, RAG systems, agentic AI solutions, and high-quality ML pipelines.
- Define AI quality metrics, evaluation frameworks, golden datasets, and systematic evaluation loops to improve accuracy, faithfulness, safety, and reliability.
- Build safety guardrails, observability, and human-review workflows for secure, large-scale production AI systems.
- Advise customer data science and engineering leaders and communicate complex technical concepts to technical and executive audiences.
- Collaborate with Snowflake Product and Engineering teams to provide field feedback and shape the AI platform.
- Create reusable reference architectures, evaluation harnesses, and deployment patterns across customer engagements.
- Spend at least 25% of time onsite with strategic customers and travel frequently within the United States.
Requirements
- 7+ years of professional software engineering experience.
- Demonstrated experience leading technical projects or teams, setting technical direction, reviewing work, and driving delivery to completion.
- Proven experience building and productionizing applications using LLMs, particularly RAG and agentic workflows.
- Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems and using evaluations to improve quality over time.
- Experience in a customer-facing technical role and willingness to travel.
- Strong problem-solving and communication skills, comfort with ambiguity, and ability to independently execute complex open-ended work.
- Preferred experience building evaluation sets from production traces and synthetic data and running A/B tests, ablations, and offline evaluations.
- Preferred familiarity with Braintrust, LangSmith, Arize, Weave, Promptfoo, or custom evaluation harnesses.
- Preferred experience analyzing failure modes in agent or RAG systems and improving them with targeted evaluations.
- Preferred hands-on MLOps experience covering model deployment, monitoring, and evaluation in AWS, Azure, or GCP.
- Preferred familiarity with pandas, numpy, and Snowpark.
- Startup or high-growth environment experience is preferred.
Benefits
- The role is customer-facing and requires frequent travel, with at least 25% of time onsite at customer locations within the United States.
- Snowflake employees must follow confidentiality and security standards and protect customer information.
- Salary and benefits information is provided on the Snowflake Careers Site for United States jobs.
Tech Stack
Categories
Forward Deployed
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.