OPT Machine Learning Jobs
Machine learning roles are among the most OPT-friendly in tech. Most positions require a master's or PhD in computer science, statistics, or a related field, and STEM OPT extensions apply, giving you up to three years of authorized work. Employers in this space file H-1B visa petitions at high rates, making ML a strong long-term visa path.
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Get Access To All JobsINTRODUCTION
We are looking for the best
About us
42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles. We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.
About The Role
We are building next-generation vehicle intelligence at 42dot, enabling vehicles to understand user intent, trip context, vehicle state, environmental conditions, and system constraints, then coordinate vehicle behavior to deliver personalized, proactive, transparent, and trustworthy experiences. As an ML / AI Engineer, you will design and develop AI-driven vehicle intelligence features that help users drive farther, feel more confident, reduce cognitive load, and experience vehicles that adapt to their needs. You will work across vehicle telemetry, user behavior, navigation, energy usage, thermal systems, cabin comfort, charging, simulation, and fleet data to build intelligent systems that can predict, recommend, plan, and optimize vehicle behavior. This role is focused on applying modern AI and machine learning technologies, including reinforcement learning, multimodal AI, foundation models, large language models, personalization, time-series forecasting, planning, simulation-based learning, and on-device inference. Reinforcement learning will be an important intelligence algorithm for developing adaptive vehicle behaviors, optimizing system-level decisions, and improving vehicle experiences through simulation, fleet feedback, and real-world operating data. You will also collaborate closely with autonomous driving and VLA engineers to connect, integrate, and combine vehicle intelligence with driving intelligence. This role is not focused on developing core VLA models, but it will help define how user intent, trip goals, vehicle constraints, energy targets, comfort preferences, and system-level recommendations are shared with VLA and autonomous driving systems.
Responsibilities
- Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints.
- Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation.
- Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data.
- Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control.
- Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment.
- Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations.
- Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness. Work cross-functionally with Product, UX, Systems Engineering and Controls.
QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Electrical Engineering, or a related technical field.
- 3+ years of experience developing and deploying machine learning or AI solutions in production environments.
- Strong experience with machine learning frameworks and programming languages such as Python, PyTorch, TensorFlow, JAX, or similar tools.
- Experience building predictive, optimization, recommendation, forecasting, or personalization models using large-scale real-world datasets.
- Solid understanding of reinforcement learning, time-series modeling, statistical learning, and data-driven decision-making systems.
PREFERRED QUALIFICATIONS
- Experience applying reinforcement learning, planning, simulation-based learning, or optimization techniques to complex real-world systems.
- Familiarity with vehicle systems, connected vehicles, mobility platforms, automotive software, energy management, charging systems, or intelligent transportation technologies.
- Experience working with multimodal AI, foundation models, large language models (LLMs), agent-based systems, or personalized AI experiences.
- Knowledge of deploying ML models to edge or embedded platforms, including considerations for latency, compute efficiency, safety, reliability, and on-device inference.
- Experience collaborating with cross-functional teams including product, UX, controls, systems engineering, autonomous driving, or robotics organizations to bring AI-powered features into production.
※ Please review the following information before applying.
How to work in 42dot, About 42dot Way →
INTRODUCTION
We are looking for the best
About us
42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles. We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.
About The Role
We are building next-generation vehicle intelligence at 42dot, enabling vehicles to understand user intent, trip context, vehicle state, environmental conditions, and system constraints, then coordinate vehicle behavior to deliver personalized, proactive, transparent, and trustworthy experiences. As an ML / AI Engineer, you will design and develop AI-driven vehicle intelligence features that help users drive farther, feel more confident, reduce cognitive load, and experience vehicles that adapt to their needs. You will work across vehicle telemetry, user behavior, navigation, energy usage, thermal systems, cabin comfort, charging, simulation, and fleet data to build intelligent systems that can predict, recommend, plan, and optimize vehicle behavior. This role is focused on applying modern AI and machine learning technologies, including reinforcement learning, multimodal AI, foundation models, large language models, personalization, time-series forecasting, planning, simulation-based learning, and on-device inference. Reinforcement learning will be an important intelligence algorithm for developing adaptive vehicle behaviors, optimizing system-level decisions, and improving vehicle experiences through simulation, fleet feedback, and real-world operating data. You will also collaborate closely with autonomous driving and VLA engineers to connect, integrate, and combine vehicle intelligence with driving intelligence. This role is not focused on developing core VLA models, but it will help define how user intent, trip goals, vehicle constraints, energy targets, comfort preferences, and system-level recommendations are shared with VLA and autonomous driving systems.
Responsibilities
- Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints.
- Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation.
- Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data.
- Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control.
- Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment.
- Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations.
- Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness. Work cross-functionally with Product, UX, Systems Engineering and Controls.
QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Electrical Engineering, or a related technical field.
- 3+ years of experience developing and deploying machine learning or AI solutions in production environments.
- Strong experience with machine learning frameworks and programming languages such as Python, PyTorch, TensorFlow, JAX, or similar tools.
- Experience building predictive, optimization, recommendation, forecasting, or personalization models using large-scale real-world datasets.
- Solid understanding of reinforcement learning, time-series modeling, statistical learning, and data-driven decision-making systems.
PREFERRED QUALIFICATIONS
- Experience applying reinforcement learning, planning, simulation-based learning, or optimization techniques to complex real-world systems.
- Familiarity with vehicle systems, connected vehicles, mobility platforms, automotive software, energy management, charging systems, or intelligent transportation technologies.
- Experience working with multimodal AI, foundation models, large language models (LLMs), agent-based systems, or personalized AI experiences.
- Knowledge of deploying ML models to edge or embedded platforms, including considerations for latency, compute efficiency, safety, reliability, and on-device inference.
- Experience collaborating with cross-functional teams including product, UX, controls, systems engineering, autonomous driving, or robotics organizations to bring AI-powered features into production.
※ Please review the following information before applying.
How to work in 42dot, About 42dot Way →
See all 1,297+ OPT Machine Learning Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship in Machine Learning
Target companies with active H-1B filing histories
Companies that consistently file H-1B visa petitions for ML roles are your best bet for long-term sponsorship. Check OFLC disclosure data for employers with recent machine learning or data science LCA filings to confirm they have an established sponsorship process.
Lead with your STEM OPT timeline
Most ML employers plan hiring on multi-year horizons. Telling recruiters you have up to three years of STEM OPT remaining signals you're not a short-term hire. Frame it as runway, not a constraint, when discussing your work authorization.
Specialize before you apply
Generalist ML resumes get lost. Employers hiring for NLP, computer vision, reinforcement learning, or MLOps want demonstrated depth. Pick a specialization aligned with your coursework or research and build your portfolio and resume around that specific area.
Quantify your model impact in every bullet
ML hiring managers screen for results, not methods. Replace vague descriptions like 'built a classification model' with metrics: accuracy improvements, latency reductions, or business outcomes your model drove. Numbers move resumes past initial filters faster than technical jargon.
Use your research or thesis as a portfolio anchor
If your OPT authorization stems from a graduate program, your thesis or research project is a legitimate work sample. Link to papers, GitHub repositories, or Kaggle notebooks that demonstrate real ML work. Academic output carries weight with technical recruiters.
Apply to mid-size companies, not just large tech firms
Large tech companies attract thousands of OPT applicants for ML roles. Mid-size companies with ML infrastructure needs, such as fintech, healthtech, or autonomous systems firms, often sponsor visas with less competition and faster hiring timelines.
Machine Learning OPT: Frequently Asked Questions
Can I work in machine learning on OPT without employer sponsorship?
Yes, during your OPT period you're authorized to work without your employer filing any petition on your behalf. You just need the role to be directly related to your degree field, which for ML typically means a degree in computer science, statistics, electrical engineering, or a related STEM discipline. Sponsorship only becomes relevant when transitioning to a long-term visa like the H-1B.
Does a machine learning job qualify for the STEM OPT extension?
It does if your degree is on the STEM Designated Degree Program List and the role is directly related to that degree. Most ML positions require quantitative or computer science backgrounds, which are almost universally STEM-designated. Your employer also needs to be enrolled in E-Verify to support the extension, so confirm that before accepting an offer.
Where can I find machine learning jobs that are open to OPT students?
Migrate Mate is built specifically for F-1 OPT students and filters for employers who are open to hiring candidates on work authorization. Searching for machine learning roles on Migrate Mate surfaces positions where sponsorship history or OPT-friendliness has already been factored in, which saves time compared to applying broadly and discovering authorization issues late in the process.
What happens to my OPT if my machine learning role is eliminated or I'm laid off?
You have a 90-day unemployment allowance across your standard OPT period. If you're on the STEM OPT extension, the allowance increases to 150 days total. You must find a new qualifying ML role within that window. The new employer must also be E-Verify enrolled if you're on the extension. Report any employer changes to your DSO promptly to keep your SEVIS record current.
Can I work as a machine learning contractor or freelancer on OPT?
Self-employment on OPT is permitted but requires that the work is directly related to your degree. For ML, that means genuine technical work, not general business activities. You must be able to document the relationship between your degree and the work performed. On STEM OPT, self-employment faces additional restrictions, and your DSO should confirm your specific situation before you structure any freelance arrangement.