STEM OPT ML Research Engineer Jobs
ML Research Engineer roles in deep learning, NLP, and computer vision qualify for the 24-month STEM OPT extension if your degree is in computer science, electrical engineering, statistics, or a related STEM field. Your employer must be enrolled in E-Verify, and you'll need a signed I-983 training plan before your extension begins.
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Get Access To All JobsSUMMARY
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.
Local Pay Range
$16,000—$21,000 USD
ABOUT DATABRICKS
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.
BENEFITS
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.
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.
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.
Local Pay Range
$16,000—$21,000 USD
ABOUT DATABRICKS
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.
BENEFITS
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.
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.
See all 742+ STEM OPT ML Research Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT ML Research Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a ML Research Engineer
Verify your degree CIP code first
Check that your institution's program CIP code maps to an approved STEM field before you apply for the extension. Your DSO can confirm the code. Mismatches between your degree field and the STEM OPT list are a common reason extensions get rejected.
Confirm E-Verify enrollment before accepting
Search the E-Verify employer database directly on the E-Verify website before you sign an offer. Many research labs, university spin-outs, and early-stage AI startups are not enrolled, which disqualifies them from hiring you on STEM OPT.
Target your I-983 to ML research deliverables
Your I-983 training plan must describe concrete learning objectives tied to your role. For ML Research Engineer positions, this means listing specific model architectures, research methodologies, or publication goals rather than vague phrases like 'improve machine learning skills.'
Check prevailing wage before negotiating your offer
Run your job title and work location through the OFLC Wage Search to see the DOL wage level for ML Research Engineer roles. This figure sets the floor for what E-Verify employers must pay you, so knowing it strengthens your negotiating position.
Use Migrate Mate to find verified STEM OPT employers
Filter for ML Research Engineer openings on Migrate Mate, which surfaces employers with confirmed E-Verify enrollment. This cuts out the manual verification step and helps you focus your applications on companies that can legally hire you on STEM OPT.
File your extension application before your OPT EAD expires
USCIS must receive your I-765 extension application before your current EAD end date. For ML roles with competitive offer timelines, submit your paperwork as soon as your employer signs the I-983, not after you've accepted the offer.
Frequently Asked Questions
Does an ML Research Engineer role qualify for the STEM OPT extension?
ML Research Engineer is classified under computer and information research scientists (SOC code 15-1221) in O*NET, which falls within an approved STEM category. Your degree must be in a qualifying field such as computer science, electrical engineering, applied mathematics, or statistics. Confirm your program's CIP code with your DSO before filing.
What does my employer need to do to hire me on STEM OPT?
Your employer must be enrolled in E-Verify and must sign Form I-983, the training plan that documents your learning objectives and supervision structure. The I-983 must be completed before your extension application is submitted to USCIS. Employers who are not enrolled in E-Verify cannot hire you on STEM OPT, regardless of role or company size.
What goes into the I-983 training plan for an ML Research Engineer?
The I-983 must describe specific goals tied to your ML Research Engineer responsibilities: the techniques you'll learn, the research problems you'll work on, and how your supervisor will evaluate your progress. Generic descriptions get flagged. Tie each objective to a concrete deliverable, such as a published paper, a deployed model, or a research milestone.
How does cap-gap protection apply if I'm on STEM OPT and get selected in the H-1B lottery?
If you're on STEM OPT and your employer files an H-1B visa petition for you before your EAD expires, cap-gap automatically extends your work authorization through September 30 of that year. If your H-1B is approved with an October 1 start date, you're covered continuously. Your employer must file before your EAD end date for cap-gap to apply.
Where can I find ML Research Engineer jobs with employers already enrolled in E-Verify?
Migrate Mate lists ML Research Engineer roles and filters for employers with confirmed E-Verify enrollment, so you don't have to manually check each company before applying. This matters because research labs, university affiliates, and AI startups vary widely in their E-Verify status, and applying to an unenrolled employer wastes your STEM OPT window.