Staff Machine Learning Engineer

About the role

P-1504

The Applied AI team at Databricks sits at the forefront of advancing GenAI-powered products. Over the past years, we’ve launched Databricks Assistant, AI/BI Genie, and Agent Bricks working with product teams, and made significant strides in LLM quality for these products. These products are used by 100s of thousands of Databricks users every day. We are tackling challenging problems like code suggestion, error detection and correction, text-to-sql generation, automatic pipeline generation, knowledge QA and many others.

As our GenAI products continue to evolve, we are seeking multiple GenAI Engineers from junior levels to more senior levels to drive the next phase of development. In 2025, we will focus on enhancing LLM quality, expanding GenAI capabilities across Databricks products, and strengthening our platform architecture to enable seamless AI interactions at scale.

Key Responsibilities

- Shape the direction of our applied AI areas and intelligence features in our products. Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services (e.g., Databricks Assistant and AI/BI Genie).

- Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains.

- Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration.

- Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction.

- Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance.

What We’re Looking For

- 2-8 years of machine learning engineering experience in high-velocity, high-growth companies. Alternatively, a strong background in relevant ML research in academia will be considered as an equivalent qualification.

- Strong track record of working with language modeling technologies. This could include the following: Developing generative and embedding techniques, modern model architectures, fine tuning / pre-training datasets, and evaluation benchmarks.

- Proficiency in Python, TensorFlow/PyTorch, and scalable ML architectures.

- Ability to drive end-to-end model development, from research and prototyping to deployment and monitoring.

- Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences.

- Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment.

- Experience with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) is a bonus.