RDQ127R59

Summary

As a Senior Applied ML Engineer on the Applied AI team at Databricks, you will use machine learning, scheduling, and optimization algorithms to maximize the efficiency and performance of our infrastructure. Your work will span the entire stack—from cluster management down to query compilation. You will solve complex, high-impact engineering problems to deliver highly optimized, cost-effective workloads for our customers.

Impact You Will Have

• Accelerate Serverless Growth: Drive the scaling and efficiency of Databricks serverless compute products through advanced optimization techniques.
• Build Systems: Design end-to-end ML4Sys solutions from the ground up within a lean team of domain experts to support
• Shape Strategy: Define the roadmap for applied ML investments by collaborating with engineering and product leaders across Databricks.
• Drive Deployment: Architect, train, and deploy state-of-the-art models that directly improve product performance and cost efficiency.
• Scale Infrastructure: Build robust ML pipelines, data processing layers, model serving components, and production monitoring systems to help scale
• Innovate: Research and implement novel modeling techniques tailored specifically to computer systems and distributed environments.

Minimum Qualifications

• Education: Background in Computer Science and Master’s degree in Machine Learning, Data Science, or a related computational field (AI, Bioinformatics, EE, Physics, etc).
• ML Experience: Strong background in building, training, and deploying machine learning models in production.
• Infrastructure Knowledge: Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks.
• Core Coding: Proficiency in Python, Scala, or Java.

Preferred Skills

• Advanced Education: PhD in AI, Data Science, or a related technical discipline.
• Industry Experience: 4+ years of machine learning engineering experience in high-velocity, high-growth environment.
• Systems Domain: Strong understanding of computer architecture, distributed computing, cloud compute, database internals, or networking.
• Optimization: Experience with operations research, forecasting, markov decision processes, or other optimization algorithms for sequential decision making.
• Scale: Proven track record of optimizing large-scale distributed systems or cloud infrastructure via data-driven approaches.