STEM OPT AI ML Engineer Jobs
AI ML Engineer roles sit squarely within STEM OPT eligibility, giving you up to 24 additional months of work authorization beyond your initial OPT period. Your employer must be enrolled in E-Verify, and your degree in computer science, statistics, or a related STEM field must align with the role's CIP code.
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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 763+ STEM OPT AI ML Engineer Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as an AI ML Engineer
Verify your CIP code before applying
Pull your degree's CIP code from your I-20 and cross-reference it against the STEM Designated Degree Program List. AI ML Engineer roles typically align with CIP codes in computer science or electrical engineering, but a mismatch can make your extension ineligible.
Confirm E-Verify enrollment before accepting offers
Ask your recruiter for the company's E-Verify employer ID or check directly through the E-Verify employer search. An employer who isn't enrolled can't legally employ you on STEM OPT, regardless of how eager they are to hire.
Target employers with active ML infrastructure roles
Focus your search on companies posting roles tied to model training pipelines, MLOps, or production inference systems. These signal teams with dedicated ML headcount, which means real STEM OPT training plans rather than ad-hoc project assignments that can weaken your I-983.
Search STEM OPT AI roles on Migrate Mate
Migrate Mate filters AI ML Engineer listings by E-Verify enrollment status, so you're not manually vetting each employer. Run your search there to narrow directly to companies already set up to hire STEM OPT students.
Draft your I-983 training plan around measurable ML goals
Work with your DSO and hiring manager to structure the I-983 around concrete deliverables like model accuracy benchmarks or deployment milestones. USCIS scrutinizes vague training plans, and specific technical objectives tied to your degree's learning outcomes strengthen your extension case.
Understand cap-gap coverage if your H-1B is selected
If your H-1B visa registration is selected and you're still on STEM OPT when October 1 arrives, cap-gap automatically extends your work authorization. Your employer doesn't need to take any additional action, but you should confirm your EAD end date and selection notice timing with your DSO.
Frequently Asked Questions
Does an AI ML Engineer role qualify for the STEM OPT extension?
Yes, if your degree is in a STEM-designated field such as computer science, statistics, mathematics, or electrical engineering and the role's job duties align with that field. The O*NET occupation code for AI ML Engineer roles typically falls under computer occupations, which STEM-designated CIP codes commonly map to. Confirm the match with your DSO before filing the extension.
What E-Verify requirement applies to my STEM OPT employer?
Your employer must be actively enrolled in E-Verify before you can work on a STEM OPT extension. Enrollment isn't just registration: the company must have a current Memorandum of Understanding with E-Verify in place. You can verify enrollment through the E-Verify employer search tool. If your employer isn't enrolled, they cannot legally employ you under STEM OPT, and the extension will not be valid.
What goes into the I-983 training plan for an AI ML Engineer?
The I-983 must document a structured training program directly tied to your STEM degree. For AI ML roles, that means listing specific learning objectives like deploying scalable inference systems, fine-tuning large language models, or implementing MLOps pipelines. Vague descriptions like 'assist with machine learning projects' won't satisfy USCIS review. Your employer's supervising engineer or manager typically co-signs the plan, and your DSO must recommend the extension before you submit.
How does cap-gap work if I'm on STEM OPT when my H-1B starts?
If your H-1B petition is filed before your STEM OPT expires and your registration is selected, cap-gap automatically extends your work authorization through September 30 of that year, or until your H-1B takes effect on October 1. You don't need a new EAD during this period. Your employer should continue your employment without interruption, but notify your DSO immediately upon H-1B selection so your SEVIS record reflects the cap-gap status.
Where can I find AI ML Engineer jobs that accept STEM OPT students?
Migrate Mate is the recommended starting point. It filters AI ML Engineer listings by E-Verify enrollment status, so you're searching a pool of employers already set up to hire STEM OPT students rather than manually checking each company. From there, you can cross-reference roles against the OFLC Wage Search to understand prevailing wage levels for the position before negotiating your offer.