
About Databricks
Empowering data teams with unified analytics
Key Highlights
- Headquartered in San Francisco, CA
- Valuation of $43 billion with $3.5 billion raised
- Serves over 7,000 customers including Comcast and Shell
- Utilizes Apache Spark for big data processing
Databricks, headquartered in San Francisco, California, is a unified data analytics platform that simplifies data engineering and collaborative data science. Trusted by over 7,000 organizations, including Fortune 500 companies like Comcast and Shell, Databricks has raised $3.5 billion in funding, ac...
🎁 Benefits
Databricks offers competitive salaries, equity options, generous PTO policies, and a remote-friendly work environment. Employees also benefit from a l...
🌟 Culture
Databricks fosters a culture of innovation with a strong emphasis on data-driven decision-making. The company values collaboration across teams and en...
Skills & Technologies
Overview
Databricks is hiring a Staff Software Engineer to enhance their Search Ranking capabilities. You'll lead the development of ML-based search relevance models and collaborate with cross-functional teams. This role requires 10+ years of experience in search relevance systems.
Job Description
Who you are
You have a strong academic background with a BS+ in Computer Science, and ideally a Master's or PhD. With over 10 years of experience, you've developed search relevance systems at scale, demonstrating your ability to handle complex production environments. Your expertise in machine learning and natural language processing allows you to design and implement automated ML pipelines effectively. You thrive in collaborative settings, working closely with product managers and cross-functional teams to drive technology-first initiatives that align with business strategies.
You possess a deep understanding of query understanding and ranking systems, enabling you to contribute to building robust frameworks for evaluating search ranking improvements. Your experience in developing and deploying ML-based systems ensures that you can lead enhancements to search quality effectively. You are passionate about advancing AI/ML-powered products and are eager to make a significant impact in the search and discovery experience at Databricks.
Desirable
Experience with large-scale data processing and familiarity with Databricks' products will be advantageous. A background in high-impact research related to search relevance will also set you apart. You are comfortable with rapid experimentation and iteration, which is crucial for driving innovation in search technologies.
What you'll do
In this role, you will drive the development and deployment of machine learning-based search and discovery relevance models integrated with Databricks' products and services. You will design and implement automated ML and NLP pipelines for data preprocessing, query understanding, ranking, and retrieval, enabling rapid experimentation and iteration. Collaborating with product managers and cross-functional teams, you will help shape technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
You will contribute to building a robust framework for evaluating search ranking improvements, both offline and online. Your leadership will be crucial in enhancing search ranking, improving query understanding, and growing the coverage of assets to enable seamless search at scale. You will also mentor junior engineers and contribute to the overall growth of the team, fostering a culture of innovation and excellence.
What we offer
At Databricks, you will be part of a dynamic team that is at the forefront of AI/ML advancements. We offer a collaborative work environment where your contributions will directly impact our customers' experiences. You will have access to cutting-edge technologies and the opportunity to work on high-impact projects that shape the future of search and discovery. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.
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