Senior Applied ML Engineer - ML4Sys

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.

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range

$16,000—$21,000 USD

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.