OPT ML Engineer Jobs
ML Engineer jobs are among the most OPT-friendly roles in tech. Most positions require a master's or PhD in computer science, machine learning, or a related field, which aligns well with STEM OPT extension eligibility and gives you up to 36 months of authorized work experience to secure H-1B visa sponsorship.
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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 as a ML Engineer
Target STEM-designated programs from the start
Confirm your degree program carries a STEM designation before applying. ML Engineer roles almost universally qualify for the 24-month STEM OPT extension, giving you three years total to build experience and secure H-1B visa sponsorship.
Prioritize companies with an active H-1B filing history
Search USCIS and DOL disclosure data for employers who have sponsored ML Engineers before. A company with zero prior H-1B filings in technical roles is a real risk when your OPT clock is running.
Raise sponsorship in the offer negotiation stage, not earlier
Wait until you have an offer in hand before discussing H-1B sponsorship. Raising it during screening signals uncertainty and can eliminate you early, especially when competing against candidates who don't need authorization.
Frame your ML research as direct production value
Recruiters at sponsoring companies care about deployment, not just experimentation. Connect your thesis work, research projects, or academic publications to real-world applications like recommendation systems, NLP pipelines, or computer vision at scale.
Apply to Series B and later startups, not just large tech firms
Growth-stage companies often sponsor ML Engineers because the talent pool is thin and the visa cost is a small fraction of the role's value. Many OPT students overlook this tier and limit themselves to big tech.
File your STEM OPT extension well before your initial OPT expires
USCIS recommends filing at least 90 days before expiration. A timely filing preserves your work authorization automatically during processing. Missing the window creates gaps that can disqualify you from roles mid-interview process.
ML Engineer OPT: Frequently Asked Questions
Can I work as an ML Engineer on OPT without H-1B sponsorship?
Yes. OPT authorizes full-time employment in a role directly related to your degree, so you can work as an ML Engineer immediately after graduation. The H-1B becomes relevant once your OPT period ends. If your degree is STEM-designated, you have up to 36 months total before needing a different visa status.
Do ML Engineer roles typically qualify for the STEM OPT extension?
Almost all ML Engineer positions qualify, because the role requires applied knowledge from a STEM field and the work is directly tied to that degree. The key requirement is that your academic program holds a STEM designation, not the job title itself. Degrees in computer science, electrical engineering, statistics, and data science all commonly qualify.
Where can I find ML Engineer jobs that are open to OPT students?
Migrate Mate is built specifically for F-1 OPT and visa-sponsored candidates. You can browse ML Engineer listings filtered by sponsorship willingness, which saves significant time compared to applying broadly and discovering late in the process that a company won't sponsor. It's the most direct way to focus your search on employers who are already open to hiring international talent.
Does my ML Engineer role need to match my degree field exactly to count toward OPT?
It needs to be directly related to your degree, but an exact title match isn't required. A computer science graduate working as an ML Engineer satisfies the requirement clearly. If your degree is in a neighboring field like applied mathematics or statistics, you'll want to document how the role draws on that academic background in case your DSO or USCIS asks.
What happens to my OPT if my ML Engineer job ends before I find a new one?
You have a cumulative 90-day unemployment allowance during your OPT period, and an additional 60 days during the STEM extension, for a total of 150 days. These periods don't reset between jobs. You must report any employment gap to your DSO promptly, and your next role still needs to be directly related to your degree field.