STEM OPT Machine Learning Jobs
Machine Learning roles in data science, NLP, and computer vision fall under STEM-designated CIP codes, making them eligible for the 24-month STEM OPT extension beyond your initial 12 months. Your employer must be enrolled in E-Verify and sign an I-983 training plan before your DSO can authorize the extension.
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At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard — from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.
As a Senior Machine Learning Engineer on the State Estimation and Mapping (SEAM) organization, you will develop and improve the ML perception model that powers the secondary (fallback) autonomy stack for Super Cruise 3. You will focus on building robust perception from multi-modal camera, lidar, and radar data so the vehicle can safely bring itself to a stop when the primary autonomy stack is unavailable. You will lead the design, implementation, and continuous improvement of ML models for object detection, segmentation, tracking, and prediction, working closely with partner teams across perception, planning, controls, and safety.
- Design, train, and evaluate ML perception models for object detection, semantic/instance segmentation, tracking, and short-horizon prediction using multi-modal camera, lidar, and radar data.
- Develop and maintain the secondary stack perception model that enables the fallback autonomy system to safely bring the vehicle to a minimal risk condition when the primary system experiences a fault.
- Define clear ML success metrics (e.g., precision/recall, latency, robustness under edge cases) and drive systematic experimentation to improve model performance against those metrics.
- Analyze large-scale datasets, curate challenging scenarios, and build data selection and labeling strategies that improve robustness for long-tail and degraded-sensor conditions.
- Implement efficient training and inference pipelines, including model optimization techniques (e.g., pruning, quantization, distillation) to meet on-vehicle compute and latency budgets.
- Collaborate with software and infra engineers to integrate models into production systems, including interfaces, configuration, deployment, monitoring, and regression safeguards.
- Partner with Safety, Systems Engineering, and Product to translate system requirements into concrete ML model requirements, metrics, and validation criteria.
- Contribute to verification and validation strategies for the fallback perception model, including offline evaluation, simulation, hardware-in-the-loop, and on-road testing.
- Participate in code reviews, promote ML and software engineering best practices, and provide technical mentorship to other engineers.
Qualifications
- BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field; or equivalent practical experience building ML perception systems.
- 3–5 years of experience developing ML solutions in perception, prediction, and/or autonomous driving or related domains.
- Strong experience with multi-modal sensor data (camera, lidar, radar), including data preprocessing, synchronization, and fusion.
- Deep expertise in modern deep learning for perception, such as convolutional and transformer-based architectures for:
- 2D/3D object detection
- Semantic and instance segmentation
- Multi-object tracking and motion prediction
- Proficiency in at least one major ML framework (e.g., PyTorch, TensorFlow, JAX) and Python for model development, training, and analysis.
- Solid software engineering skills, including experience working in C++ or similar languages in large, collaborative codebases.
- Demonstrated ability to define ML metrics, design experiments, and systematically improve model performance and robustness.
- Strong problem-solving, communication, and cross-functional collaboration skills.
- Self-motivated, with a passion for autonomous driving technology and its potential impact on safety and mobility.
Nice to have
- Experience deploying ML models on embedded or resource-constrained platforms, including model optimization and performance tuning for real-time inference.
- Experience with AV/ADAS perception stacks, robotics, or ROS.
- Familiarity with safety-critical systems and development practices.
- Experience with large-scale data pipelines, labeling workflows, and experiment management for ML.
LOCATION
Remote: This role is based remotely but if you live within a 50-mile radius of Atlanta, Austin, Detroit, Warren, Milford or Mountain View, you are expected to report to that location three times per week, at minimum.
Compensation
The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.
- The salary range for this role is $170,600.00 to $261,300.00. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. This job may be eligible for relocation benefits.
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Job Description
At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard — from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.
As a Senior Machine Learning Engineer on the State Estimation and Mapping (SEAM) organization, you will develop and improve the ML perception model that powers the secondary (fallback) autonomy stack for Super Cruise 3. You will focus on building robust perception from multi-modal camera, lidar, and radar data so the vehicle can safely bring itself to a stop when the primary autonomy stack is unavailable. You will lead the design, implementation, and continuous improvement of ML models for object detection, segmentation, tracking, and prediction, working closely with partner teams across perception, planning, controls, and safety.
- Design, train, and evaluate ML perception models for object detection, semantic/instance segmentation, tracking, and short-horizon prediction using multi-modal camera, lidar, and radar data.
- Develop and maintain the secondary stack perception model that enables the fallback autonomy system to safely bring the vehicle to a minimal risk condition when the primary system experiences a fault.
- Define clear ML success metrics (e.g., precision/recall, latency, robustness under edge cases) and drive systematic experimentation to improve model performance against those metrics.
- Analyze large-scale datasets, curate challenging scenarios, and build data selection and labeling strategies that improve robustness for long-tail and degraded-sensor conditions.
- Implement efficient training and inference pipelines, including model optimization techniques (e.g., pruning, quantization, distillation) to meet on-vehicle compute and latency budgets.
- Collaborate with software and infra engineers to integrate models into production systems, including interfaces, configuration, deployment, monitoring, and regression safeguards.
- Partner with Safety, Systems Engineering, and Product to translate system requirements into concrete ML model requirements, metrics, and validation criteria.
- Contribute to verification and validation strategies for the fallback perception model, including offline evaluation, simulation, hardware-in-the-loop, and on-road testing.
- Participate in code reviews, promote ML and software engineering best practices, and provide technical mentorship to other engineers.
Qualifications
- BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field; or equivalent practical experience building ML perception systems.
- 3–5 years of experience developing ML solutions in perception, prediction, and/or autonomous driving or related domains.
- Strong experience with multi-modal sensor data (camera, lidar, radar), including data preprocessing, synchronization, and fusion.
- Deep expertise in modern deep learning for perception, such as convolutional and transformer-based architectures for:
- 2D/3D object detection
- Semantic and instance segmentation
- Multi-object tracking and motion prediction
- Proficiency in at least one major ML framework (e.g., PyTorch, TensorFlow, JAX) and Python for model development, training, and analysis.
- Solid software engineering skills, including experience working in C++ or similar languages in large, collaborative codebases.
- Demonstrated ability to define ML metrics, design experiments, and systematically improve model performance and robustness.
- Strong problem-solving, communication, and cross-functional collaboration skills.
- Self-motivated, with a passion for autonomous driving technology and its potential impact on safety and mobility.
Nice to have
- Experience deploying ML models on embedded or resource-constrained platforms, including model optimization and performance tuning for real-time inference.
- Experience with AV/ADAS perception stacks, robotics, or ROS.
- Familiarity with safety-critical systems and development practices.
- Experience with large-scale data pipelines, labeling workflows, and experiment management for ML.
LOCATION
Remote: This role is based remotely but if you live within a 50-mile radius of Atlanta, Austin, Detroit, Warren, Milford or Mountain View, you are expected to report to that location three times per week, at minimum.
Compensation
The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.
- The salary range for this role is $170,600.00 to $261,300.00. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. This job may be eligible for relocation benefits.
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
See all 765+ STEM OPT Machine Learning Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Machine Learning Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization in Machine Learning
Verify your degree's CIP code eligibility
Check that your degree program's CIP code appears on the STEM Designated Degree Program List published by USCIS. Computer Science (11.07), Electrical Engineering (14.10), and Statistics (27.05) all qualify, but not every data-adjacent program does.
Confirm E-Verify enrollment before applying
Machine Learning roles at startups and research labs often lack E-Verify enrollment, which disqualifies them from STEM OPT. Search the E-Verify employer search tool by company name before submitting any application.
Target employers with active ML-specific LCA filings
Use Migrate Mate to filter for employers with verified Labor Condition Application filings under SOC codes like 15-2051 (Data Scientists) and 15-1252 (Software Developers). These companies have already navigated STEM OPT and understand the I-983 obligation.
Negotiate your I-983 training plan before signing
Your offer letter is not enough: USCIS requires a signed I-983 detailing your ML learning objectives, supervision structure, and how the role relates to your degree. Raise this document with HR during the offer stage, not after your start date.
Check prevailing wage against the OFLC Wage Search
Your employer must pay at least the DOL prevailing wage for your occupation and location. Run your job title and county through the OFLC Wage Search to catch underpaid offers early, since a below-wage role can jeopardize future H-1B visa sponsorship.
Build a portfolio aligned with O*NET ML task definitions
O*NET defines Machine Learning Engineers and Data Scientists by specific tasks: model training, feature engineering, and deployment pipelines. Structure your GitHub portfolio and resume around those task definitions so your application maps cleanly to the specialty occupation standard.
Frequently Asked Questions
Which STEM degrees qualify for the STEM OPT extension in Machine Learning roles?
Degrees in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, and Data Science typically qualify, provided their CIP code appears on the STEM Designated Degree Program List published by USCIS. A Machine Learning or AI-specific master's program qualifies if the CIP code is listed. Degrees in Business Analytics or Information Systems may not qualify, so confirm your CIP code with your DSO before targeting STEM OPT positions.
Does every Machine Learning employer need to be enrolled in E-Verify?
Yes, E-Verify enrollment is a hard requirement for STEM OPT. No employer exemptions exist regardless of company size, funding stage, or role seniority. Many early-stage AI startups and university spin-outs are not yet enrolled, so verify enrollment directly through the E-Verify employer search before accepting an offer. Migrate Mate filters for E-Verify-enrolled employers so you can focus on companies that are already eligible.
What goes into the I-983 training plan for a Machine Learning role?
The I-983 must describe your learning objectives in relation to your degree, identify your direct supervisor, outline how the ML work connects to your field of study, and include a schedule for self-evaluations every six months. For Machine Learning roles, training plans typically document objectives around model development, research methodologies, and software engineering practices. Your employer signs the form and your DSO endorses it before USCIS authorizes your EAD extension.
How does cap-gap protection apply if my employer files for H-1B during my STEM OPT period?
If your employer files a timely H-1B petition before your STEM OPT EAD expires and you are selected in the lottery, cap-gap automatically extends your work authorization through September 30 of that fiscal year. You can continue working as a Machine Learning engineer during this period without a new EAD. If your petition is not selected, your STEM OPT authorization continues until its original expiration date, assuming the petition was filed before that date.
How do I find Machine Learning jobs where employers already understand STEM OPT requirements?
Search Migrate Mate for Machine Learning roles filtered by E-Verify-enrolled employers with active LCA filing history under relevant SOC codes. Employers who have previously filed LCAs for data science or software engineering roles are more likely to have internal processes for onboarding STEM OPT students and completing the I-983 training plan without delays.