Staff Machine Learning Engineer
-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.
What this role actually needs.
-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. 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. - 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. Requirements: - 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. Company context: Databricks is the data and AI lakehouse platform used by the world's largest enterprises for analytics, ML, and generative AI.
Day-to-day expectations
Databricks lists these responsibilities for the Staff Machine Learning Engineer role.
- 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.
- 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.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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.
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Source: databricks.com · Source ID: 8401114002 · Confidence: 93/100 · Last checked: May 7, 2026
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