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CSQ226R125
The Machine Learning (ML) Practice team is a highly specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, as well as put those pipelines into production. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in LLMs, MLOps, and ML more broadly. This role can be remote.
The impact you will have:
- Develop LLM solutions on customer data, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
- Help customers solve tough problems across industries like Health and Life Sciences, Finance, Retail, Startups, and many others
- Build, scale, and optimize customer data science workloads across industries and apply best-in-class MLOps to productionize these workloads
- Advise data teams on data science architecture, tooling, and best practices
- Provide thought leadership by presenting at conferences such as Data+AI Summit and mentoring the larger ML SME community in Databricks
- Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap
What we look for:
- Experience in building Generative AI applications, including RAG, agents, Text2SQL, fine-tuning, and deploying LLMs, using tools such as HuggingFace, Langchain, and OpenAI
- 2-8 years of hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, MLflow, scikit-learn, and PyTorch
- Experience in building production-grade ML or GenAI deployments on AWS, Azure, or GCP.
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
- Experience in communicating and teaching technical concepts to both non-technical and technical audiences
- Passion for collaboration, life-long learning, and driving business value through ML
- [Preferred] Experience working with Databricks and Apache Spark™
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
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