STEM OPT Machine Learning Intern Jobs
Machine Learning Intern roles qualify for the STEM OPT 24-month extension when your degree falls under an eligible CIP code and your employer is enrolled in E-Verify. With up to 36 months of total OPT work authorization, you have real runway to build ML experience while staying compliant with USCIS training plan requirements.
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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 727+ STEM OPT Machine Learning Intern Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Machine Learning Intern Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Machine Learning Intern
Verify your CIP code before applying
Check your degree's CIP code against the official STEM Designated Degree Program List to confirm STEM OPT eligibility. Computer science, statistics, and electrical engineering codes commonly support ML intern roles, but data science program codes vary by institution.
Confirm E-Verify enrollment before accepting offers
Search the E-Verify employer database to confirm a company is actively enrolled before signing an offer. Startups and research labs frequently hire ML interns but may not yet be enrolled, which disqualifies them as STEM OPT employers.
Draft your I-983 around measurable ML deliverables
Your I-983 training plan must link specific learning objectives to your ML intern work. Frame deliverables around model evaluation metrics, dataset pipelines, or deployment tasks rather than vague project descriptions that DSOs commonly flag for revision.
Target employers with established research infrastructure
Use Migrate Mate to filter for employers with active STEM OPT hiring history in machine learning and data science roles. Companies running internal research teams or university partnerships are structured to support the E-Verify and I-983 compliance process.
Apply early if your OPT end date overlaps cap season
If your initial OPT expires between April and September, cap-gap protection can extend your authorization through September 30 while an H-1B visa petition is pending. Submit your STEM OPT extension application to USCIS at least 90 days before your EAD expires.
Use O*NET to align your training plan job zone
Look up the Machine Learning Engineer or Data Scientist occupation in O*NET to identify the job zone and typical education requirements. Use that language in your I-983 to demonstrate the role requires your STEM degree rather than general technical skills.
Frequently Asked Questions
Does a Machine Learning Intern role qualify for the STEM OPT extension?
It qualifies if your degree is on the STEM Designated Degree Program List and your employer is enrolled in E-Verify. Machine learning intern positions typically require a background in computer science, statistics, applied mathematics, or a related STEM field. Your DSO confirms eligibility based on your specific CIP code, not the job title alone.
What does an employer need to do to hire you on STEM OPT?
The employer must be enrolled in E-Verify and co-sign your I-983 training plan before your STEM OPT extension begins. The I-983 requires them to describe your learning objectives, supervision structure, and how the role relates to your STEM degree. Employers who haven't hired STEM OPT students before often need guidance on this step.
How do you find Machine Learning Intern roles from employers already enrolled in E-Verify?
Migrate Mate lets you search machine learning intern positions filtered by employers with verified STEM OPT hiring history. You can also cross-reference a company's E-Verify status directly through the E-Verify employer search before applying, which saves time if you're targeting smaller research teams or early-stage companies.
What happens to your work authorization if your OPT expires during H-1B cap season?
If your employer files an H-1B petition on your behalf before your OPT expires, cap-gap protection extends your work authorization through September 30 of that fiscal year. You can continue working in your Machine Learning Intern or full-time ML role during this period without interruption, provided the petition remains pending or approved.
Can a part-time Machine Learning Internship count toward STEM OPT?
USCIS requires STEM OPT employment to be at least 20 hours per week with a qualifying E-Verify employer. A part-time ML internship at or above that threshold is generally acceptable, but the I-983 training plan must reflect a genuine learning experience tied to your STEM degree, not a minimal or administrative role.