Senior Applied ML Engineer - ML4Sys

Databricks|03 Aug 2026

fulltimeonsite

Before you apply

Listed location: San Francisco, California

Work arrangement: onsite. A remote label does not confirm worldwide eligibility or visa sponsorship.

Read the employer’s description for qualifications, compensation and work eligibility. Confirm the position is still open on the application page.

Job description supplied by Databricks; category and skill labels may be inferred. How our listings work · Report a problem

Job Description

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.

Pay Range Transparency

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

$166,000—$210,250 USD

About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

Benefits

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.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Skills mentioned

PythonScalaJavaDistributed SystemsDatabricks

Frequently asked questions

Is the Senior Applied ML Engineer - ML4Sys position at Databricks remote?

The Senior Applied ML Engineer - ML4Sys role at Databricks does not have a confirmed remote arrangement in our data. Check the employer description for its work location.

What type of employment is the Senior Applied ML Engineer - ML4Sys role?

Databricks is hiring for a full-time Senior Applied ML Engineer - ML4Sys position.

Which skills are mentioned for the Senior Applied ML Engineer - ML4Sys job at Databricks?

Detected skill labels include Python, Scala, Java, Distributed Systems, Databricks. Check the employer description to distinguish required skills from preferred experience.