Staff Machine Learning Engineer
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will develop and deploy AI models and systems, create data-collection and LLM technologies, and build machine-learning pipelines for experimentation. You will deliver scalable backend systems, logging, telemetry, and evaluation harnesses while taking models from research and prototyping through deployment and monitoring.
Requirements
- 2–8 years of machine learning engineering experience, or equivalent relevant ML research experience
- Experience with language modeling technologies, generative and embedding techniques, model architectures, training datasets, and evaluation benchmarks
- Proficiency in Python, TensorFlow or PyTorch, and scalable ML architectures
- Ability to drive end-to-end model development
- Software engineering knowledge including testing, code review, and deployment
- LLM fine-tuning, prompt engineering, and retrieval-augmented generation are a bonus
Responsibilities
- Develop and deploy AI models and systems for product capabilities
- Develop data-collection, fine-tuning, and LLM technologies
- Design and implement ML pipelines for preprocessing, feature engineering, training, tuning, and evaluation
- Collaborate with researchers, ML engineers, and product teams to deliver AI solutions
- Build scalable backend systems, logging, telemetry, and evaluation harnesses
- Drive model development from research and prototyping through deployment and monitoring