Medical Devices OPT Jobs in New York
Medical devices F-1 OPT sponsorship jobs in New York are concentrated around the Hudson Valley, Long Island, and New York City, where companies like Stryker, Philips, and Becton Dickinson maintain significant operations. New York's combination of world-class hospital systems and a dense biotech corridor makes it one of the stronger states for international students in this industry.
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A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
The Structure-Function ML group in Basel within Prescient Design (AI4DD), CoE, a division devoted to developing machine learning-based methods for de novo antibody design, seeks exceptional researchers who have a demonstrated research background in machine learning and protein structural biology and design, a passion for independent research and technical problem-solving, and a proven ability to develop and implement ideas from research into production. We are looking for a very talented Machine Learning Scientist to join Prescient Design/AI4DD. The successful candidate will contribute to our antibody design efforts, partner with biologists, technologists and drug discoverers to develop new machine learning methods for de novo protein design with special application to protein therapeutics.
In this role, you will:
- Develop cutting-edge machine learning methods for modeling biological data, focusing on structural biology.
- Deliver deep learning-based software solutions that accelerate drug discovery and therapeutic development in support of our de novo antibody design and lab-in-the-loop efforts.
- Collaborate with AI/ML scientists and form close working relationships with global research teams.
- Write structured, tested, and maintainable code while participating in proactive code reviews.
- Actively shape and contribute to our collaborative and innovative team culture.
- Partner with biologists and technologists to develop new methods for de novo protein design.
Who you are
- You hold an M.S. or PhD in Computer Science, Statistics, Physics, or a related technical field and possess 1+ years of hands-on experience designing and training machine learning models on large datasets.
- You have published on denovo antibody design in relevant journals like Nature Biotechnology, Neurips, or ICML.
- You are proficient in Python and at least one deep learning framework like PyTorch, TensorFlow, or JAX.
- You have experience with using MLOps frameworks like Hydra and Weights & Biases.
- You have a public codebase of computational denovo antibody design (available on e.g. GitHub).
- You have demonstrated experience with modern techniques, including hallucination or folding models.
- You bring prior experience or familiarity working with antibody sequence and structure data, which is a plus.
- You are an excellent communicator, fluent in English, with a passion for driving projects in cross-functional environments.
Relocation benefits are NOT available for this job posting.
The expected salary range for this position based on the primary location of New York is $141,100 - 262,100 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
ComputationCoE
tech4lifeComputationalScience
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
JOB FACTS
- Job Sub Category: Artificial Intelligence & Machine Learning
- Schedule: Full time
- Job Type: Regular
- Posted Date: Jul 24th 2026
- Job ID: 202607-119350
The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
The Structure-Function ML group in Basel within Prescient Design (AI4DD), CoE, a division devoted to developing machine learning-based methods for de novo antibody design, seeks exceptional researchers who have a demonstrated research background in machine learning and protein structural biology and design, a passion for independent research and technical problem-solving, and a proven ability to develop and implement ideas from research into production. We are looking for a very talented Machine Learning Scientist to join Prescient Design/AI4DD. The successful candidate will contribute to our antibody design efforts, partner with biologists, technologists and drug discoverers to develop new machine learning methods for de novo protein design with special application to protein therapeutics.
In this role, you will:
- Develop cutting-edge machine learning methods for modeling biological data, focusing on structural biology.
- Deliver deep learning-based software solutions that accelerate drug discovery and therapeutic development in support of our de novo antibody design and lab-in-the-loop efforts.
- Collaborate with AI/ML scientists and form close working relationships with global research teams.
- Write structured, tested, and maintainable code while participating in proactive code reviews.
- Actively shape and contribute to our collaborative and innovative team culture.
- Partner with biologists and technologists to develop new methods for de novo protein design.
Who you are
- You hold an M.S. or PhD in Computer Science, Statistics, Physics, or a related technical field and possess 1+ years of hands-on experience designing and training machine learning models on large datasets.
- You have published on denovo antibody design in relevant journals like Nature Biotechnology, Neurips, or ICML.
- You are proficient in Python and at least one deep learning framework like PyTorch, TensorFlow, or JAX.
- You have experience with using MLOps frameworks like Hydra and Weights & Biases.
- You have a public codebase of computational denovo antibody design (available on e.g. GitHub).
- You have demonstrated experience with modern techniques, including hallucination or folding models.
- You bring prior experience or familiarity working with antibody sequence and structure data, which is a plus.
- You are an excellent communicator, fluent in English, with a passion for driving projects in cross-functional environments.
Relocation benefits are NOT available for this job posting.
The expected salary range for this position based on the primary location of New York is $141,100 - 262,100 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
ComputationCoE
tech4lifeComputationalScience
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
JOB FACTS
- Job Sub Category: Artificial Intelligence & Machine Learning
- Schedule: Full time
- Job Type: Regular
- Posted Date: Jul 24th 2026
- Job ID: 202607-119350
OPT Medical Devices Job Roles in New York
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Search Medical Devices Jobs in New YorkMedical Devices OPT Sponsorship Jobs in New York: Frequently Asked Questions
Which medical devices companies sponsor F-1 OPT visas in New York?
Several major medical devices employers with New York operations have a track record of hiring F-1 OPT workers, including Becton Dickinson (which has a significant presence in the Hudson Valley), Philips Healthcare, Stryker, and smaller medtech firms clustered on Long Island. Hospital-affiliated device divisions at institutions like NYU Langone and NewYork-Presbyterian also hire OPT candidates in clinical engineering and device evaluation roles.
Which cities in New York have the most medical devices F-1 OPT sponsorship jobs?
New York City, particularly its biotech and health-tech corridors in Manhattan and Brooklyn, sees the highest volume of medical devices postings for OPT candidates. Long Island, especially the Nassau-Suffolk corridor, hosts a dense cluster of device manufacturers and distributors. The Hudson Valley, anchored by companies like Becton Dickinson in Franklin Lakes just across the state line, also draws many New York-based applicants.
What types of medical devices roles typically qualify for F-1 OPT sponsorship?
Roles directly tied to a degree field are best positioned for OPT authorization. In medical devices, these commonly include quality engineering, regulatory affairs, R&D engineering, biomedical engineering, clinical applications, and software development for device platforms. Positions requiring a bachelor's or advanced degree in biomedical, mechanical, electrical, or computer engineering align most clearly with OPT eligibility requirements, as the role must be directly related to the student's field of study.
How do I find medical devices F-1 OPT sponsorship jobs in New York?
Migrate Mate is built specifically for international students and filters jobs by visa type, industry, and state, so you can search directly for medical devices roles in New York that are open to F-1 OPT candidates without sorting through listings that don't apply to your situation. Filtering by OPT on Migrate Mate surfaces employers who have sponsored international candidates in this sector before, saving significant research time.
Are there any New York-specific considerations for F-1 OPT in the medical devices industry?
New York requires medical devices companies to comply with both federal FDA regulations and state Department of Health requirements, which means regulatory affairs and quality assurance roles are in consistent demand, giving OPT candidates with those specializations a practical edge. Students using STEM OPT extensions, common in biomedical and engineering fields, should confirm their employer has a formal training plan in place, as New York-based employers vary in how prepared they are for the STEM OPT reporting requirements.