Staff Software Engineer - Snowflake Feature Store
AI in this role
Build and scale cutting-edge machine learning feature store and serving infrastructure as a Staff Software Engineer at Snowflake.
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.
Join our ML Feature Store team where we're building cutting-edge product capabilities that power complex feature transformations and low latency feature serving. We're revolutionizing machine learning feature management and serving capabilities as part of the Snowflake ML suite of products. In the era of GenAI and agents, our team delivers high-quality, fresh feature solutions that make a real difference for our customers.
IN THIS ROLE AT SNOWFLAKE, YOU WILL:
Help define and own the roadmap for Snowflake Feature Store, working collaboratively with senior architects and ML team leadership
Build and execute a vision for incorporating new advances in machine learning
Ensure operational excellence of services and meet reliability, availability, and performance commitments
Collaborate across ML partner teams to improve development velocity and capabilities
Support team members in delivering high technical quality
WE WOULD LOVE TO HEAR FROM YOU IF YOU HAVE:
10+ years of experience in designing and building data serving infrastructure and/or machine learning platforms.
Preferred: Strong operational experience managing PostgreSQL in production environments.
Strong track record working with machine learning systems and platforms.
Strong understanding of computer science fundamentals.
B.Sc. in Computer Science
Fluency in Java and Python
Experience with feature engineering platforms and ML platforms
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
How we rate this
Staff Software Engineer - Snowflake Feature Store at Snowflake rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where data serving was part of your work. What did you do?
- Tell me about a project where feature store was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Walk me through how you've used Python in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Machine Learning, Data Serving, Feature Store, Distributed Systems, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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