Staff Machine Learning Engineer - Cortex Code Quality

Staff Machine Learning Engineer - Cortex Code Quality 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. Snowflake is about empowering enterprises to achieve their full potential and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology and careers to the next level. About the Role The Cortex Code team is building the future of coding agents for working with data. See our flagship product in action: Cortex Code in Action: Live Demos + AMA https://www.youtube.com/watch?v=YLGL0MU5AXQ. As a Staff MLE/AI Engineer on Cortex Code Quality, you will help define architect agent behavior at enterprise scale by building the agentic systems and methodology that make our users build cutting edge agentic systems that are efficient, repeatable, auditable, and shippable. You’ll partner with modeling, platform, and product leadership to turn customer pain into golden scenarios, metrics, and experiment loops that the whole team can trust. What you will do in this role: - Agent strategy & systems: Own major pillars of the quality stack: tuning agent behavior to engage on next generation agentic coding tasks. - Hill-climb infrastructure: Design and evolve pipelines and tooling that support large-scale experimentation, error mining, and iteration on prompts/tools/workflows with clear before/after signals. - Deep analysis & prioritization: Lead postmortems on quality regressions; cluster failure modes; translate findings into a prioritized roadmap for engineering and modeling partners. - Cross-functional leadership: Align product, infra, and applied AI on what “good” means for critical customer workflows; mentor engineers and uplevel eval craft across the team. - Production-minded rigor: Ensure quality systems are dependable in practice—reproducible runs, stable datasets, versioning, and operational clarity when things drift. Requirements: - Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field. Master’s or higher preferred but not a requirement. - 8+ years of experience shipping AI/ML-backed software in production, including Staff-level ownership of technical direction, cross-team delivery, and mentoring. - Strong track record building and operating eval harnesses, measurement, and/or experimentation loops for LLM/agent systems—not only one-off benchmarks. - Proficiency in programming languages such as Python, TypeScript, Go (strong in at least two). - Exceptional communication skills: crisp writeups, constructive debate, and ability to influence without authority across engineering and product. - (Optional) Experience with data engineering pipelines (dbt, Airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus. Nice to have - Deep experience with agentic coding tools (IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits. - Background in data engineering (dbt, Airflow), analytics, retrieval / RAG, or semantic layers—highly relevant for data-centric coding agents. - Prior work on LLM observability, safety/guardrails, or quality systems used as release gates in production. You may be a particularly good fit if you - Have built and owned complex quality + data pipelines—subs ... (truncated, view full listing at source)