Principal Machine Learning Engineer - Search Quality at Snowflake

Role Overview

As a Principal Machine Learning Engineer specializing in Search Quality at Snowflake, you will be a technical leader responsible for revolutionizing how we measure and improve search relevance across our expanding product ecosystem. The Snowscope team is at the heart of enabling users to find relevant information within Snowflake's vast landscape of data and metadata. You will play a pivotal role in transforming our search relevance methodologies from heuristic-based approaches to a disciplined, data-driven framework. You will be instrumental in bridging the gap between traditional search and modern AI techniques, ensuring our search technology is prepared for the next generation of AI-driven agentic workflows.

A Day in the Life

Your day-to-day activities will involve:

• Leading the technical direction for Search Quality initiatives.
• Designing and implementing evaluation frameworks (e.g., NDCG, MRR) for search relevance.
• Conducting A/B tests to measure the impact of search improvements.
• Collaborating with Product Management and Data Science teams to define quality metrics.
• Researching and implementing state-of-the-art machine learning techniques for search, including Learning to Rank (LTR), query understanding, and personalized ranking.
• Optimizing search systems to handle Snowflake-scale data and user traffic.
• Staying abreast of the latest advancements in NLP, LLMs, and their application to Information Retrieval.
• Mentoring and guiding other engineers on the team.
• Contributing to the long-term vision for Universal Search at Snowflake.

Why Menlo Park, CA?

Menlo Park, California, is located in the heart of Silicon Valley, offering unparalleled access to the world's leading technology companies, research institutions, and venture capital firms. The area boasts a vibrant tech community, numerous networking opportunities, and a high concentration of talented engineers and data scientists. Menlo Park offers a high quality of life with excellent schools, beautiful parks, and a mild climate. Being in close proximity to San Francisco and other Bay Area cities, you'll have access to a wide range of cultural attractions, dining options, and outdoor activities.

Career Path

This Principal Machine Learning Engineer role provides a strong foundation for career advancement within Snowflake. Potential career paths include:

• Senior Principal Engineer: Leading larger teams and more complex projects.
• Staff Engineer: Focusing on technical leadership and architectural design across multiple teams.
• Engineering Manager: Leading and managing a team of engineers.
• Principal Data Scientist: Focusing on advanced data science and machine learning research.
• Architect: Defining the overall technical architecture for Snowflake's search systems.

Salary & Benefits

Snowflake offers a competitive salary and benefits package, commensurate with experience and qualifications. The estimated salary range for this role is $180,000 to $350,000 per year. In addition to salary, Snowflake provides a comprehensive benefits package, including:

• Health, dental, and vision insurance
• Generous paid time off and holidays
• Employee Stock Purchase Program (ESPP)
• 401(k) retirement plan with company match
• Professional development opportunities
• Wellness programs

Innovation Culture

Snowflake fosters a culture of innovation, collaboration, and impact. We encourage our employees to think big, move fast, and challenge the status quo. We are committed to providing our employees with the resources and support they need to succeed. Our culture is all-in on impact, innovation, and collaboration, making Snowflake the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.

How to Apply

Interested candidates are encouraged to apply online through the Snowflake careers website. Please submit your resume and a cover letter highlighting your relevant experience and qualifications.

FAQ

1. What is the Snowscope team's mission?

   The Snowscope team is focused on building and maintaining the internal search system that powers discovery across diverse corpuses, including the Catalog, Marketplace, Documentation, Workspaces, Notebooks, and more. We also maintain Universal Search, providing a seamless, single-entry search experience across all categories.

2. What are the key technologies used by the Snowscope team?

   The Snowscope team utilizes a range of technologies, including Lucene/Elasticsearch/OpenSearch, vector databases, NLP libraries, LLMs, and various machine learning frameworks.

3. What is Retrieval-Augmented Generation (RAG)?

   RAG is a technique that combines information retrieval with generative models to improve the quality and relevance of generated text. It involves retrieving relevant information from a knowledge base and using it to augment the generation process.

4. What is Learning to Rank (LTR)?

   LTR is a machine learning technique used to rank search results based on their relevance to a given query. It involves training a model to predict the relevance of each document and then using that model to rank the results.

5. What is the difference between semantic and syntactic search?

   Semantic search focuses on understanding the meaning and intent behind a query, while syntactic search focuses on matching keywords and phrases. Hybrid search combines both approaches to achieve better results.

6. What are the key metrics used to evaluate search quality?

   Key metrics include NDCG (Normalized Discounted Cumulative Gain), MRR (Mean Reciprocal Rank), precision, recall, and click-through rate (CTR).

7. What is the interview process like?

   The interview process typically involves a phone screening, a technical interview, and an on-site interview with members of the Snowscope team and other stakeholders.

8. What are the opportunities for professional development at Snowflake?

   Snowflake offers a variety of professional development opportunities, including training programs, conferences, and mentorship programs.

9. What is the work-life balance like at Snowflake?

   Snowflake is committed to providing a supportive and flexible work environment that allows employees to balance their work and personal lives.

10. What is the company culture like at Snowflake?

    Snowflake fosters a culture of innovation, collaboration, and impact. We encourage our employees to think big, move fast, and challenge the status quo.