Senior Machine Learning Engineer

Build the future of the AI Data Cloud. Join the Snowflake team.

There is only one Data Cloud. Snowflake’s founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn’t stop there. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow.

We’re at the forefront of the data revolution, committed to building the world’s greatest data and applications platform. Our ‘get it done’ culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration.

We’re looking for a Senior Machine Learning Engineer to join Snowflake’s Corporate Machine Learning team. In this role, you will design, build, and optimize scalable systems that power AI & ML driven solutions across Snowflake’s business data. This is a high-impact engineering role focused on taking AI & ML applications from prototype to production, partnering closely with ML and Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions.

ON THE Corporate ML TEAM AT SNOWFLAKE, YOU WILL:

• Provide technical & thought leadership; designing and implementing advanced machine learning techniques, focusing on robust & scalable solutions. Participate in all stages of development, ideation to production.

• Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability.

• Operationalize ML models: Partner with ML Scientists to translate models into efficient, reliable pipelines and services, enabling seamless deployment and monitoring in production environments.

• Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions — from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining.

• Develop hands-on; analyze large amounts of data, manage data quality, design & develop complex ML models (and the ensuing ML solutions) including ML pipelines, deploy & manage production-grade applications end-to-end, and tell the story in a compelling manner.

• Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs.

• Champion MLOps best practices: Establish & maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models.

• Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams.

• Mentor and guide: Provide technical mentorship to junior engineers and scientists on engineering practices and production workflows.

OUR IDEAL CANDIDATE WILL HAVE:

• High levels of curiosity, eager enthusiasm & demonstrable experience working on open-ended problems

• Strong software engineering foundations, with expertise in Python and experience developing production-quality systems using best practices in testing, modularity, and documentation.

• Deep experience in MLOps and ML infrastructure, including model deployment, serving, CI/CD pipelines, containerization and orchestration.

• Experience deploying AI/ML models in production, working with large-scale datasets, streaming data, or real-time inference systems.

• Hands-on experience with cloud-native data and compute platforms (Snowflake, AWS/GCP/Azure), including resource optimization and cost-aware design.

• Experience with machine learning or statistical modeling on large datasets; pre-processing, data quality, feature engineering; 

• Understanding of modern AI applications, including deploying Gen-AI/LLM-based solutions with techniques like RAG, prompt chaining, or agentic workflows.

• A bias toward action and ownership, with the ability to take vague requirements and turn them into high-quality, scalable engineering solutions. Adaptability, to respond to fast-evolving project scope, adjusting strategies & plans accordingly; comfortable being scrappy.

• Excellent collaboration and communication skills, able to work across disciplines and present complex ideas to both technical and non-technical audiences.

• Demonstrate keen senses of ownership, collaboration and mentorship; with the ability to inspire the team & lead a project to completion.

Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.

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