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 JobsThe Role
We are seeking a highly motivated Machine Learning Engineer to join our core research and development team, focused on video understanding and segmentation. In this role, you will build the systems that let us search, decompose, and describe massive volumes of egocentric and human-robot video at scale — turning raw, unstructured footage into structured, searchable, and richly annotated training data. You will work across video/image embedding models, LLM-based video understanding, and agentic pipelines that orchestrate multiple models into end-to-end workflows. This is a foundational role that directly shapes the data quality and scalability of our entire training data platform.
Responsibilities
- Build and optimize video/image embedding pipelines using CLIP-style and other vision-language embedding models to power large-scale, multi-modal video search and retrieval.
- Develop LLM-based video understanding systems for semantic indexing, summarization, and question-answering over long-form egocentric and third-person video.
- Design and implement instruction-level and action-level video chunking/segmentation algorithms that decompose long videos into structured, temporally-aligned clips.
- Build automated video captioning systems that combine vision-language models and LLMs to produce fine-grained, temporally-grounded descriptions of actions and scenes.
- Architect agentic systems and orchestration pipelines that chain embedding, captioning, retrieval, and LLM reasoning steps into reliable, end-to-end video understanding workflows.
- Develop and scale video search infrastructure (vector indexing, retrieval, ranking) to support semantic and multi-modal queries over millions of video clips.
- Collaborate with annotation, data engineering, and robotics teams to integrate video understanding outputs into downstream training pipelines for embodied AI and robot learning.
- Evaluate and benchmark embedding models, LLMs, and agentic frameworks against production needs; track frontier research and bring relevant techniques into the platform.
- Contribute to internal tooling, documentation, patents, and open-source initiatives where applicable.
- Mentor junior engineers and interns, and help shape the long-term technical roadmap for video understanding.
Minimum Qualifications
- MS or PhD in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of hands-on experience in computer vision or multi-modal machine learning, with direct experience in video understanding tasks.
- Strong proficiency in Python and PyTorch, with solid software engineering fundamentals.
- Hands-on experience with CLIP or similar vision-language/video embedding models for retrieval or representation learning.
- Experience building or fine-tuning LLM-based systems for video/image understanding (e.g., captioning, video QA, summarization).
- Familiarity with agentic system design — tool use, multi-step reasoning, and orchestration frameworks (e.g., LangChain, LlamaIndex, or custom agent loops).
- Experience working with large-scale video data pipelines and vector search/retrieval infrastructure (e.g., FAISS, Milvus, or equivalent).
Preferred Qualifications
- PhD with a research focus in video understanding, multi-modal learning, or vision-language models.
- Experience with temporal action segmentation, action localization, or instruction-level video chunking algorithms.
- Experience working with egocentric video datasets or head-mounted-device (HMD) captured data.
- Track record of deploying production-scale video search or retrieval systems.
- Experience integrating foundation or vision-language models (e.g., CLIP, VideoCLIP, RT-1/VLA variants) into perception or decision-making pipelines.
- Publications in top-tier computer vision or ML venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, etc).
- Experience with humanoid robotics or embodied AI data pipelines is a plus.
The Role
We are seeking a highly motivated Machine Learning Engineer to join our core research and development team, focused on video understanding and segmentation. In this role, you will build the systems that let us search, decompose, and describe massive volumes of egocentric and human-robot video at scale — turning raw, unstructured footage into structured, searchable, and richly annotated training data. You will work across video/image embedding models, LLM-based video understanding, and agentic pipelines that orchestrate multiple models into end-to-end workflows. This is a foundational role that directly shapes the data quality and scalability of our entire training data platform.
Responsibilities
- Build and optimize video/image embedding pipelines using CLIP-style and other vision-language embedding models to power large-scale, multi-modal video search and retrieval.
- Develop LLM-based video understanding systems for semantic indexing, summarization, and question-answering over long-form egocentric and third-person video.
- Design and implement instruction-level and action-level video chunking/segmentation algorithms that decompose long videos into structured, temporally-aligned clips.
- Build automated video captioning systems that combine vision-language models and LLMs to produce fine-grained, temporally-grounded descriptions of actions and scenes.
- Architect agentic systems and orchestration pipelines that chain embedding, captioning, retrieval, and LLM reasoning steps into reliable, end-to-end video understanding workflows.
- Develop and scale video search infrastructure (vector indexing, retrieval, ranking) to support semantic and multi-modal queries over millions of video clips.
- Collaborate with annotation, data engineering, and robotics teams to integrate video understanding outputs into downstream training pipelines for embodied AI and robot learning.
- Evaluate and benchmark embedding models, LLMs, and agentic frameworks against production needs; track frontier research and bring relevant techniques into the platform.
- Contribute to internal tooling, documentation, patents, and open-source initiatives where applicable.
- Mentor junior engineers and interns, and help shape the long-term technical roadmap for video understanding.
Minimum Qualifications
- MS or PhD in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of hands-on experience in computer vision or multi-modal machine learning, with direct experience in video understanding tasks.
- Strong proficiency in Python and PyTorch, with solid software engineering fundamentals.
- Hands-on experience with CLIP or similar vision-language/video embedding models for retrieval or representation learning.
- Experience building or fine-tuning LLM-based systems for video/image understanding (e.g., captioning, video QA, summarization).
- Familiarity with agentic system design — tool use, multi-step reasoning, and orchestration frameworks (e.g., LangChain, LlamaIndex, or custom agent loops).
- Experience working with large-scale video data pipelines and vector search/retrieval infrastructure (e.g., FAISS, Milvus, or equivalent).
Preferred Qualifications
- PhD with a research focus in video understanding, multi-modal learning, or vision-language models.
- Experience with temporal action segmentation, action localization, or instruction-level video chunking algorithms.
- Experience working with egocentric video datasets or head-mounted-device (HMD) captured data.
- Track record of deploying production-scale video search or retrieval systems.
- Experience integrating foundation or vision-language models (e.g., CLIP, VideoCLIP, RT-1/VLA variants) into perception or decision-making pipelines.
- Publications in top-tier computer vision or ML venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, etc).
- Experience with humanoid robotics or embodied AI data pipelines is a plus.
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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.