Build the future of data. Join the Snowflake team.

Snowflake Machine Learning team’s mission is to build easy to use, integrated and capable machine learning offerings natively within the Snowflake Data Cloud. 

We are building predictive analytics that are extremely easy to use by our existing data customers. Some examples for product areas include text analytics, predictive modeling for classification, building systems to convert unstructured data into structured and so on.

We are looking for a hands-on technical lead who is strong in building machine learning infrastructure, and understands the end to end machine learning process.

Location: San Mateo

RESPONSIBILITIES:

• This role will help define and deliver a major machine learning product area. 
• Collaboratively build and execute a vision for incorporating new advances in machine learning in ways that best achieve the team’s business objectives
• Be obsessed with customer feedback and drive product iterations to win more usage
• Own the technical architecture to enable training, evaluation, fine-tuning and launch models that identify optimal actions and predictions
• Collaborate and influence / direct technical teams and build alignment on features key scenarios to enable ML at scale at Snowflake.
• Debug production issues and ensure high technical quality
• Mentor and lead a team of ML engineers, research scientists and PMs
• Set the execution pace, team rhythms and partner with your manager and PM to ensure a high quality team and product.

IDEAL REQUIREMENTS & QUALIFICATIONS:

• Have 10+ years of software engineering experience (especially in machine learning systems)
• Strong software engineering and proficiency ideally in Java and/or Python
• Have built a roadmap and vision, and led technical decision making
• Have led and mentored  multiple engineers in delivering large, high impact projects
• Have experience with building machine learning models, including neural networks, decision trees, etc.
• Experience with several of the following frameworks: SKLearn, XGBoost, PyTorch, Tensorflow
• Thrive in ambiguity and driving 0-1 projects
• Have worked well with data scientists, business analysts and underlying infrastructure to connect the dots between business and technology partners
• Are a self-learner and continuously push the boundaries and state of the art around machine learning

BONUS POINTS FOR EXPERIENCE WITH THE FOLLOWING:

• Building systems for end to end machine learning lifecycle
• Building ML systems integrated into a database