STEM OPT AI ML Engineering Jobs
AI ML Engineering roles in computer science, data science, and related STEM fields qualify for the 24-month STEM OPT extension, giving you up to 36 months of total work authorization. Your employer must be enrolled in E-Verify to file your I-983 training plan and keep your authorization active.
Find STEM OPT AI ML Engineering JobsOverview
Showing 5 of 727+ AI ML Engineering jobs
See all 727+ AI ML Engineering Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Engineering roles.
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 727+ STEM OPT AI ML Engineering Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT AI ML Engineering Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization in AI ML Engineering
Verify your CIP code before applying
Your STEM OPT eligibility depends on your degree's Classification of Instructional Programs code matching an approved STEM list. Computer science, electrical engineering, and data science CIP codes commonly support AI ML roles, but confirm with your DSO before targeting employers.
Filter employers by E-Verify enrollment status
Only E-Verify-enrolled employers can legally hire you on STEM OPT. Before any application, confirm enrollment through the E-Verify employer search tool. Roles at non-enrolled companies, including many early-stage startups, are off-limits regardless of how strong the offer looks.
Build an I-983 training plan before the offer stage
Drafting your training plan goals for an AI ML role before you receive an offer lets you move faster once one comes. Map your learning objectives to specific ML frameworks, model development responsibilities, and performance benchmarks your employer will sign off on.
Target employers with active H-1B filing history
Companies that have consistently filed H-1B visa petitions for ML engineers are structurally prepared to support long-term authorization. Use Migrate Mate to filter AI ML Engineering roles by employers with verified sponsorship history, so you're not starting that conversation from scratch.
Align your role title with DOL wage classifications
Job titles in AI and ML vary widely, but DOL wage levels are tied to SOC codes like Software Developers or Computer and Information Research Scientists. Use the OFLC Wage Search to confirm which SOC code your offer maps to before negotiating, since misclassification can delay LCA certification.
Track your 24-month extension window against H-1B cap dates
If your STEM OPT expires before an H-1B petition takes effect, cap-gap protection may bridge the gap, but only if your employer files before April 1 of the relevant fiscal year. Coordinate your extension end date with your employer's HR team early so filing deadlines don't catch you off guard.
Frequently Asked Questions
Which STEM degrees qualify for the OPT extension for AI ML Engineering roles?
Degrees in computer science, electrical engineering, statistics, applied mathematics, and data science are among the most common qualifying fields for AI ML Engineering positions. Your degree must carry an approved STEM Classification of Instructional Programs code, and your DSO can confirm whether your specific program qualifies before you begin the extension application through USCIS.
Does my employer have to be enrolled in E-Verify to hire me on STEM OPT?
Yes. E-Verify enrollment is a hard requirement for every employer hiring STEM OPT students. There are no exceptions, even for short contracts or part-time roles. You can verify a company's enrollment status through the E-Verify employer search before accepting any offer. Working for a non-enrolled employer places your immigration status at risk.
What goes into the I-983 training plan for an AI ML Engineering position?
Your I-983 must describe the specific AI and ML skills you'll develop, the projects or responsibilities tied to those goals, how your work connects to your STEM degree, and how your employer will evaluate your progress. For AI ML Engineering roles, this typically includes model development, data pipeline work, framework proficiency, and measurable performance benchmarks signed off by a supervisor.
How does cap-gap protection work if my STEM OPT ends before my H-1B starts?
If your employer files an H-1B petition on your behalf before April 1 and your STEM OPT expires between April 1 and October 1 of the same year, cap-gap automatically extends your work authorization through September 30. Your employer must file before that deadline for protection to apply. USCIS provides guidance on cap-gap rules for F-1 students transitioning to H-1B status.
Where can I find AI ML Engineering jobs at employers already set up for STEM OPT students?
Migrate Mate lists AI ML Engineering roles filtered by employers with active E-Verify enrollment and a track record of sponsoring STEM workers. Searching there lets you focus on companies that already understand the I-983 process and are structurally prepared to support your authorization, rather than spending time educating employers who have never hired an OPT student.