OPT AI ML Platform Jobs
AI ML Platform engineering sits at the intersection of infrastructure and machine learning, making it one of the most OPT-friendly specializations in tech. Employers in this space regularly sponsor H-1B visas, and STEM OPT extensions give you up to three years of work authorization to build the track record that makes sponsorship straightforward.
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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 AI ML Platform
Target companies with dedicated ML infrastructure teams
Large tech firms and AI-focused startups with dedicated ML platform teams sponsor OPT and H-1B at high rates. Look for engineering organizations where ML infrastructure is a core product function, not a side project maintained by a single engineer.
Lead with platform impact, not just model work
Hiring managers for ML platform roles want engineers who reduce friction for data scientists at scale. Frame your experience around compute cost reduction, training throughput improvements, or pipeline reliability, not just model accuracy benchmarks from academic projects.
Get your STEM OPT extension filed before your OPT expires
Your STEM OPT extension application must be filed at least 90 days before your initial OPT end date. Missing this window means a gap in work authorization. Coordinate early with your DSO and confirm your employer qualifies under E-Verify requirements.
Specialize in a high-demand ML platform layer
Feature stores, model registries, distributed training orchestration, and inference serving are all understaffed relative to demand. Deep expertise in one of these layers makes you a stronger H-1B petition candidate because the specialty occupation requirement is easier to document.
Understand your employer's H-1B sponsorship timeline
H-1B cap-subject petitions can only be filed in April for an October start. If your OPT expires before October, ask whether the employer will file for cap-exempt status or use an academic or nonprofit partner. Clarify this before accepting an offer.
Document your work authorization status clearly in applications
When applying, state your OPT status, your STEM extension eligibility, and your authorization end date upfront. Employers unfamiliar with OPT often assume it means immediate sponsorship costs. Showing you have one to three years of self-authorized work time removes that friction.
AI ML Platform OPT: Frequently Asked Questions
Do AI ML Platform jobs typically qualify for the STEM OPT extension?
Yes. AI ML Platform roles are almost always classified under CIP codes in computer science or electrical engineering, both of which appear on the STEM Designated Degree Program list. As long as your degree is in a qualifying STEM field and your employer participates in E-Verify, you can extend your OPT by 24 months beyond the initial 12, giving you up to three years of work authorization total.
How do I find AI ML Platform jobs that are open to OPT students?
Migrate Mate is built specifically for F-1 OPT students and filters job listings by sponsorship willingness, so you're not wasting time applying to roles where OPT is a disqualifier. AI ML Platform roles are well-represented on the platform because the employers posting there are already accustomed to hiring international talent on OPT and H-1B.
Can I work on an AI ML Platform team at a startup on OPT?
Yes, but verify the startup participates in E-Verify before accepting an offer if you plan to apply for a STEM OPT extension. E-Verify enrollment is a federal requirement for STEM OPT employers, and smaller startups sometimes haven't completed it. Enrollment takes only a few days, so most employers are willing to complete it if they want to hire you.
What makes AI ML Platform a strong field for long-term visa sponsorship?
ML platform engineering is a well-defined specialty occupation with a clear degree requirement in computer science or a related engineering field, which makes H-1B petitions more straightforward to document than generalist software roles. Demand consistently exceeds supply, so employers in this space have strong financial incentives to sponsor rather than lose a qualified candidate to a competitor.
What should I do if my OPT expires before my employer's H-1B petition is approved?
If you filed your H-1B petition before your OPT expired and your status is maintained through cap-gap coverage, you may continue working while the petition is pending. Cap-gap automatically bridges the period between OPT expiration and the October 1 H-1B start date for timely-filed petitions. Confirm with your DSO that your I-20 reflects the cap-gap extension and that your employer's attorney filed before the April deadline.