OPT Machine Learning Manager Jobs
Machine Learning Manager jobs on OPT require employers who can sponsor H-1B visa or support STEM OPT extensions, since the role typically falls under Computer and Information Research as a specialty occupation. Most positions demand 5-plus years of ML experience, making early OPT the right time to target senior IC roles that lead to management tracks.
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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.
See all 866+ OPT Machine Learning Manager Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Machine Learning Manager
Confirm the role qualifies as a specialty occupation
Machine Learning Manager positions almost always require a bachelor's degree or higher in computer science, statistics, or a related field. Verify the job posting explicitly states a degree requirement, not just a preference, to ensure your OPT authorization aligns cleanly.
Target employers with an established H-1B track record
Companies that have sponsored ML engineers and data scientists before are far more likely to sponsor a Machine Learning Manager. Search employer H-1B visa disclosure data through the Department of Labor to confirm a pattern of sponsorship before investing time in applications.
Clarify your OPT timeline upfront with recruiters
Machine Learning Manager hiring cycles run long, often 8 to 12 weeks. If your OPT expiration is within six months, raise your timeline early so employers can assess whether they can complete H-1B sponsorship before your authorization lapses.
Lead with your STEM OPT extension eligibility
A STEM OPT extension gives you up to three additional years of work authorization, which meaningfully reduces urgency for employers. If your degree is in computer science, data science, or a related STEM field, mention this extension explicitly in your cover letter and recruiter conversations.
Demonstrate cross-functional leadership to differentiate yourself
Machine Learning Manager roles require leading engineers, aligning with product teams, and owning model deployment decisions. Candidates who can show tangible team leadership experience, not just technical depth, stand out and reduce employer hesitation around sponsorship investment.
Focus on companies scaling ML infrastructure, not just experimenting
Organizations at the proof-of-concept stage rarely commit to sponsorship for senior hires. Target employers actively productionizing ML systems, with dedicated ML platform teams, as they have stronger business reasons to sponsor and retain a capable manager long term.
Machine Learning Manager OPT: Frequently Asked Questions
Can I work as a Machine Learning Manager on OPT?
Yes, provided your role qualifies as a specialty occupation requiring a degree in a directly related field like computer science, electrical engineering, or applied mathematics. Machine Learning Manager positions consistently meet this standard. Your OPT authorization must remain valid, and the job must be directly related to your degree field.
Do Machine Learning Manager jobs commonly offer H-1B sponsorship?
Many do, particularly at mid-size and large technology companies that depend on ML infrastructure for their core products. Firms in sectors like cloud computing, fintech, and health tech regularly sponsor ML leadership roles. Migrate Mate filters sponsorship-friendly employers so you can focus your search on companies with a documented history of supporting international candidates.
Does my degree field affect OPT eligibility for this role?
Yes. Your OPT work must be directly related to your degree. A Machine Learning Manager role is most straightforwardly supported by degrees in computer science, data science, statistics, or electrical engineering. If your degree is adjacent, such as applied mathematics or operations research, you can still qualify, but you may need to clearly articulate the connection on your OPT reporting documentation.
How does STEM OPT extension work for Machine Learning Manager roles?
If your bachelor's, master's, or doctoral degree is in a STEM-designated field, you can apply for a 24-month STEM OPT extension after your initial 12-month OPT period. The employer must be E-Verify enrolled and agree to a formal training plan. For Machine Learning Manager roles, most qualifying employers are already E-Verify participants, making the extension straightforward to execute.
What should I do if my OPT expires before an employer completes H-1B sponsorship?
The timing risk is real for management-level roles, where hiring decisions move slowly. If an approved H-1B petition is in place before your OPT expires, you can maintain status through the cap-gap provision while waiting for October 1 employment to begin. Working closely with an immigration attorney and starting conversations with employers at least six months before expiration significantly reduces the risk of a gap.