Data Scientist / Machine Learning Engineer
Data Scientist / Machine Learning Engineer
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Job Description
CSQ326R28
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 DataAI 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
- 4 to 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
Employment Type
Full Time
