5 months ago
Base Salary
$200k - $270k/yr
Responsibilities
- Lead the full lifecycle of complex, multi-engineer customer AI engagements from scoping and architecture through deployment, monitoring, and handoff.
- Define engagement quality standards, metrics, evaluation frameworks, golden datasets, and systematic evaluation loops for AI systems.
- Lead and mentor a team of 2–6 Applied AI Engineers through design reviews, code reviews, technical guidance, and career development.
- Design, iterate, and ship scalable ML pipelines and agentic AI solutions while remaining a hands-on contributor.
- Productionize AI solutions with safety guardrails, observability, human-review workflows, monitoring, and optimization.
- Advise customer data science and engineering leadership and communicate technical concepts to technical and executive stakeholders.
- Collaborate with Product and Engineering teams to shape Snowflake’s AI platform using customer feedback.
- Create reusable reference architectures, evaluation harnesses, and other assets from recurring deployment patterns.
Requirements
- Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing work, and driving delivery to completion.
- Proven experience building and productionizing LLM applications, particularly with RAG and agentic workflows.
- Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems and using evaluations to improve quality.
- At least 5 years of professional software engineering experience.
- Experience in a customer-facing technical role.
- Preferred experience building evaluation sets from production traces and synthetic data and running A/B tests, ablations, and offline evaluations.
- Preferred familiarity with evaluation and observability tools such as Braintrust, LangSmith, Arize, Weave, or Promptfoo, or experience building custom evaluation harnesses.
- Preferred experience analyzing failure modes in agent or RAG systems and reducing them through targeted evaluations.
- Preferred hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in AWS, Azure, or GCP.
- Preferred familiarity with pandas, NumPy, and Snowpark.
- Strong communication, problem-solving, ambiguity-management, and stakeholder-advisory skills are required.
- Willingness to travel and experience in a startup or high-growth environment are preferred or required as applicable.
Benefits
- At least 25% of time spent onsite with strategic customers and willingness to travel.
- Salary and benefits information is provided on the Snowflake Careers Site for U.S.-based roles.
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.