Senior Machine Learning Engineer, Collaboration

Snowflake•Menlo Park, CA

48d

About The Position

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. We’re at the forefront of the data revolution, committed to building the world’s greatest data and applications platform. Snowflake started with a clear vision: develop a cloud data platform that is effective, affordable, and accessible to all data users. Data Cloud allows sharing live data in governed and secure ways so our customers can solve their business problems. We’re building a thriving collaborative ecosystem on Snowflake that is scalable and self-reinforcing to maximize data collaboration potential. The Opportunity As a Senior ML Engineer, you will be instrumental in designing, developing, and deploying applications leveraging ML to power generation of data products. You will work with cutting-edge ML techniques to transform complex metadata into actionable insights, enabling our users to discover and leverage data like never before. This is a unique chance to shape a product that fundamentally changes how organizations collaborate on data. This is a unique opportunity to bridge ML research and production engineering, shaping a product that fundamentally changes how organizations collaborate on data.

Requirements

7+ years of industry experience designing, building, and supporting backend large-scale data processing systems in production
Strong software engineering fundamentals: code quality, reproducibility, CI/CD best practices, debugging, testing, and documentation.
Proficiency in Python and core ML/data science frameworks such as Pandas, NumPy, Scikit-Learn, XGBoost, PyTorch, and related ecosystem tools.
Understanding of modern ML applications, including deploying Gen-AI/LLM-based solutions with techniques like RAG, prompt chaining, or agentic workflows
Demonstrated hands-on experience solving applied ML problems end-to-end: data ingestion/preprocessing, feature and model selection, training, evaluation , deployment, and monitoring.
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.
Fluency in Java or Python and SQL
BS/MS/PHD in Computer Science or related majors, or equivalent experience

Nice To Haves

Familiarity with different types of machine learning approaches (supervised, reinforcement learning, contrastive learning) and their practical constraints in large-scale, dynamic code environments
Contributions to open source ML/AI projects or scientific publications; Participations in AI/ML competitions (e.g., Kaggle)
Experience working in or with developer-facing infrastructure, build/test automation, or productionizing research prototypes

Responsibilities

Design, build, and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, prompt engineering, evaluation, and deployment.
Collaborate with cross-functional teams to identify opportunities for applying ML to real-world problems.
Monitor, evaluate and improve agent performance
Stay up to date with research, frameworks, and techniques in ML/AI to apply where relevant