CSQ325R50
This role can be remote. While candidates in the Bay Area are strongly preferred , candidates in other locations will be considered.
The Machine Learning (ML) Practice team is a 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 customers build and improve ML pipelines, and put those pipelines into production. We work with customers to help them shape their long-term initiatives working alongside engineering, product, and developer relations, and internal subject matter expert (SME) teams. The ideal candidate will enjoy being part of a broader team of technologists that love empowering customers, collaborating with teammates, and satisfying your curiosity working with the latest trends in LLMs, MLOps, and ML.
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
Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains
Advise data teams on several data science such as architecture, tooling, and best practices
Present at conferences such as Data+AI Summit
Provide technical mentorship to the larger ML Subject Matter Expert community in Databricks
Collaborate with the product and engineering teams to define priorities and influence the product roadmap
What We Look For
Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
4+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
Experience building production-grade machine learning deployments on AWS, Azure, or GCP
Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience
Experience communicating and teaching technical concepts to non-technical and technical audiences alike
Passion for collaboration, life-long learning, and driving value through ML
[Preferred] Experience working with Apache Spark to process large-scale distributed datasets
Benefits
Medical, Dental, and Vision
401(k) Plan
FSA, HSA and Commuter Benefit Plans
Equity Awards
Flexible Time Off
Paid Parental Leave
Family Planning
Fitness Reimbursement
Annual Career Development Fund
Home Office/Work Headphones Reimbursement
Employee Assistance Program (EAP)
Business Travel Accident Insurance
Mental Wellness Resources
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents 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 utilizes 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.
Zone 1 Pay Range
$124,800—$220,800 USD
Zone 2 Pay Range
$124,800—$220,800 USD
Zone 3 Pay Range
$124,800—$220,800 USD
Zone 4 Pay Range
$124,800—$220,800 USD
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.
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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