Databricks

Staff Software Engineer focused on building and operating high-scale, low-latency inference systems for foundational AI models (LLMs) at Databricks. The role involves designing and implementing core systems and APIs for model serving, optimizing performance on GPU workloads, and influencing architectural direction for the Foundation Model Serving product.

Lead the engineering team responsible for Databricks' Model Serving product, focusing on both customer-facing capabilities and foundational infrastructure for scalable, low-latency AI/ML model inference.

Databricks is seeking a Senior Software Engineer to join their Model Serving product team. This role focuses on designing and building scalable, low-latency inference systems for AI/ML models (traditional ML to LLMs) on CPU and GPU. Responsibilities include optimizing performance, throughput, autoscaling, and operational efficiency, as well as contributing to core serving infrastructure components like routing, caching, and observability. The role requires strong experience in large-scale distributed systems and model serving infrastructure.

Databricks is seeking a Staff Software Engineer to work on their Model Serving product, which is a core pillar of their platform for enterprises to deploy and manage AI/ML models. The role involves designing and building systems for high-throughput, low-latency inference across CPU and GPU workloads, influencing architectural direction, and collaborating with various teams to deliver a world-class serving platform.

Staff Data Scientist focused on Trust and Safety at Databricks, developing and implementing ML models for fraud and abuse detection, analyzing security features, and collaborating with engineering and security teams to protect the platform and customers. The role involves creating compliance solutions, gathering requirements, and guiding junior team members.

Product Manager to define and drive the vision for Databricks' enterprise AI offerings, focusing on generative AI, agents, and new workloads. The role involves partnering with engineering and research to turn AI advancements into practical products, engaging with customers to uncover use cases, and shaping the long-term strategy for Databricks AI.

Product Manager for Databricks AI, focusing on defining and driving the vision for enterprise AI, generative AI, and agents. The role involves partnering with engineering and research to turn AI advancements into practical tools, uncovering new use cases, and building products for scale and longevity. Requires strong technical background in AI/ML and experience with enterprise SaaS or developer platforms.

Databricks is seeking a Staff Software Engineer to work on their AI-powered Assistant, focusing on customer engagement and documentation platform. The role involves leveraging ML and LLMs to analyze technical use cases, identify problems, and provide solutions or facilitate escalation. Key responsibilities include leading the design, development, and deployment of systems for evaluation, answer retrieval, and quality improvement, as well as driving engineering best practices and contributing to technical planning.

Role focuses on architecting and guiding customers in building production-grade ML & AI applications, with a strong emphasis on GenAI solutions like RAG and agentic systems, and MLOps. The role involves deep technical expertise in end-to-end ML pipelines, training/inference optimization, and integrating with cloud services, serving as a trusted technical expert for customers and internal teams.

Senior Engineering Manager for Search at Databricks, responsible for building and scaling next-generation Search products including Vector Search, Keyword Search, and Search Quality. The role involves leading a team to design, build, and operate cloud-native Search services, drive innovation in search capabilities, and partner with product and research teams on roadmap definition. The position also includes managing large-scale services, mentoring engineers, and shaping the long-term strategy for Search as a core enabler for AI and data applications.

Databricks is seeking ML Engineers to enhance their AI/ML-powered Search product. The role involves developing and deploying ML-based search and discovery relevance models, designing ML/NLP pipelines for query understanding and ranking, and contributing to evaluation frameworks. Experience applying LLMs to search relevance and developing search relevance systems at scale is required.

Databricks is hiring ML Engineers for their Applied AI team to enhance search quality for their platform. The role involves developing and deploying ML-based search and discovery relevance models, building automated ML/NLP pipelines for data preprocessing, query understanding, ranking, retrieval, and model evaluation, and contributing to a robust framework for evaluating search ranking improvements. Experience applying LLMs to search relevance and developing search relevance systems at scale is required.

Staff Backline Engineer role at Databricks focused on deep-dive troubleshooting, root cause analysis, and architectural optimization within the Databricks Data and AI ecosystem. The role involves developing automated workflows and AI-driven diagnostic tools to improve supportability and scale the organization. Requires expertise in either Data Engineering, Product Supportability, or the AI track (ML/GenAI systems, LLMs, agentic workflows).

Senior Applied AI Engineer focused on applying ML to improve Databricks' engineering systems and infrastructure, including cluster management and query compilation. The role involves building end-to-end systems, deploying models at scale, and architecting ML infrastructure for production environments.

Databricks is hiring ML Engineers to enhance their Search product quality, focusing on search ranking, query understanding, and building robust evals. The role involves developing and deploying ML-based search and discovery relevance models and systems, designing ML/NLP pipelines, and collaborating with product teams. Experience with LLMs in search relevance and developing large-scale search relevance systems is required.