Senior Software Engineer Engineering Systems and AI Developer Productivity

About the position

Join the Snowflake team as a Software Engineer in a joint R&D initiative between the Engineering Systems Team and Snowflake AI Research team. This team drives innovation at the intersection of machine learning and developer productivity, with flagship projects such as ML-powered test systems and LLM-powered code review and automation tools. The team supports both product-facing ML initiatives and internal systems optimization, accelerating Snowflake's thought leadership and operational excellence in enterprise AI. This role is ideal for candidates who have deep hands-on experience in every stage of the software development lifecycle, have a vision on how AI can play a transformative role in it, and a passion for making it happen.

Responsibilities

• Partner across research and engineering to design, implement, and maintain machine learning systems that directly impact developer workflows and core platform efficiency.
• Build and optimize LLM-based tools for code review, quality automation, and developer assistance, from research prototype to robust production deployment.
• Contribute to the improvement of internal ML and data tooling, pipelines, and collaboration between AI Research and Engineering Systems.
• Operate across the software lifecycle: requirements, design, implementation, delivery, testing, iteration, and support.
• Contribute to a culture of innovation, excellence, and inclusion in a collaborative and fast-paced environment.

Requirements

• Strong passion for making developers highly productive.
• A track record of technical leadership.
• Excellent problem solving skills, and strong CS fundamentals including data structures, algorithms, and distributed systems.
• Fluency in Java, C++, Python or Go.
• Ability to work in a team environment, collaborate well, and mentor junior engineers.
• A strategic mindset and strong sense of what to prioritize for maximum impact.
• Excellent interpersonal communication skills.
• Outstanding written and oral English language skills.
• Strong software engineering fundamentals: code quality, reproducibility, CI/CD best practices, debugging, testing, and documentation.
• Familiarity with ML operations (MLOps), data/feature versioning, and collaborative software development (Git, containers, etc.).
• Strong communication and teamwork skills; able to describe technical tradeoffs and engage both research scientists and software engineers.

Nice-to-haves

• Developing data or distributed systems internals.
• Familiarity with different types of machine learning approaches (supervised, reinforcement learning, contrastive learning) and their practical constraints in large-scale, dynamic code environments.
• Proven experience with authoring roadmap documents, project plans and RCAs.
• Experience working in or with developer-facing infrastructure, build/test automation, or productionizing research prototypes.
• Experience with large-scale language models (LLMs), agentic/autoML systems, or ML for developer productivity (e.g., code intelligence, automated testing, static analysis).

Benefits

• Career development opportunities in ML engineering and advanced software systems.
• Values-driven, inclusive environment that values curiosity, impact, and technical excellence.