STEM OPT Machine Learning Scientist Jobs
Machine Learning Scientist roles sit squarely within STEM OPT eligibility, giving F-1 graduates with degrees in computer science, statistics, or related fields up to 24 additional months of work authorization beyond their initial OPT period. Your employer must be enrolled in E-Verify, and you'll need a signed I-983 training plan before your extension starts.
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Get Access To All JobsINTRODUCTION
Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech’s Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist building agents for applied small-molecule drug design. You will develop autonomous, LLM-driven agentic workflows that orchestrate ML models, physics-based methods, and cheminformatics tools to accelerate discovery, working with world-class chemists and structural biologists.
ROLE AND RESPONSIBILITIES:
- Design, build, and apply agentic workflows and ML models for key challenges in small-molecule drug design.
- Fine-tune foundation models for drug discovery relevant topics using internal and external datasets and tools.
- Optimize agent-derived hypotheses in close collaboration with world-class computational and medicinal chemists and structural biologists.
- Drive scientific impact through publications, open-source releases, and conference talks.
- Collaborate widely with computational and experimental researchers at Roche and with academic partners.
BASIC QUALIFICATIONS:
- You are experienced developing LLM-driven agents for scientific workflows and you understand how to orchestrate tools and models reliably.
- You bring strong machine-learning foundations in linear algebra, probability and optimization, with hands-on experience with GNNs, sequence/language models and reinforcement learning.
- You are fluent in Python and modern agentic coding environments such as LangChain, ML frameworks such as PyTorch or JAX, as well as cheminformatics toolkits like RDKit or OpenEye.
- You hold a PhD or equivalent research depth in machine learning, computer science, chemical engineering or a related quantitative field such as physics or statistics.
- You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab).
PREFERRED QUALIFICATIONS:
- Hands-on experience orchestrating multi-tool or multi-agent scientific pipelines.
- Hands-on experience working along the small molecule drug discovery value chain and an excitement to engage with chemists.
- Familiarity with structural biology datasets.
If you want to put autonomous AI to work discovering the medicines patients need next, apply now and help build the future of drug design at Roche.
COMPENSATION
- The expected salary range for this position based on the primary location of California for the Machine Learning Engineer is $147,600 - $274,000, and the Senior Machine Learning Engineer for California is $167,400 - $310,800. 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
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.
INTRODUCTION
Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech’s Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist building agents for applied small-molecule drug design. You will develop autonomous, LLM-driven agentic workflows that orchestrate ML models, physics-based methods, and cheminformatics tools to accelerate discovery, working with world-class chemists and structural biologists.
ROLE AND RESPONSIBILITIES:
- Design, build, and apply agentic workflows and ML models for key challenges in small-molecule drug design.
- Fine-tune foundation models for drug discovery relevant topics using internal and external datasets and tools.
- Optimize agent-derived hypotheses in close collaboration with world-class computational and medicinal chemists and structural biologists.
- Drive scientific impact through publications, open-source releases, and conference talks.
- Collaborate widely with computational and experimental researchers at Roche and with academic partners.
BASIC QUALIFICATIONS:
- You are experienced developing LLM-driven agents for scientific workflows and you understand how to orchestrate tools and models reliably.
- You bring strong machine-learning foundations in linear algebra, probability and optimization, with hands-on experience with GNNs, sequence/language models and reinforcement learning.
- You are fluent in Python and modern agentic coding environments such as LangChain, ML frameworks such as PyTorch or JAX, as well as cheminformatics toolkits like RDKit or OpenEye.
- You hold a PhD or equivalent research depth in machine learning, computer science, chemical engineering or a related quantitative field such as physics or statistics.
- You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab).
PREFERRED QUALIFICATIONS:
- Hands-on experience orchestrating multi-tool or multi-agent scientific pipelines.
- Hands-on experience working along the small molecule drug discovery value chain and an excitement to engage with chemists.
- Familiarity with structural biology datasets.
If you want to put autonomous AI to work discovering the medicines patients need next, apply now and help build the future of drug design at Roche.
COMPENSATION
- The expected salary range for this position based on the primary location of California for the Machine Learning Engineer is $147,600 - $274,000, and the Senior Machine Learning Engineer for California is $167,400 - $310,800. 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
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.
See all 397+ STEM OPT Machine Learning Scientist Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Machine Learning Scientist
Verify your CIP code matches ML roles
Your degree's Classification of Instructional Programs code must align with an approved STEM field. Check your I-20 against the DHS STEM Designated Degree Program List before applying, since a mismatch disqualifies you from the 24-month extension entirely.
Confirm E-Verify enrollment before accepting offers
Ask recruiters directly whether the company is enrolled in E-Verify before your final interview round. Employers not enrolled cannot legally employ STEM OPT students, so catching this early saves you from offer letters you can't legally accept.
Build your I-983 training plan around ML deliverables
Your training plan must tie learning objectives to concrete, measurable outcomes specific to your Machine Learning Scientist role, such as model accuracy benchmarks or deployment milestones. Generic job descriptions get flagged by DSOs and delay your extension start date.
Target employers with existing LCA filings in ML
Use Migrate Mate to filter Machine Learning Scientist roles by employers with verified DOL Labor Condition Application filings, so you're targeting companies with an established STEM OPT and H-1B visa sponsorship track record rather than starting that conversation from scratch.
File your STEM OPT extension 90 days early
USCIS allows you to apply up to 90 days before your current OPT EAD expires. File your I-765 as early as that window opens, since USCIS processing can take several months and a late application leaves a gap in your work authorization.
Negotiate your start date around your EAD card
You can't legally begin work until your STEM OPT EAD is physically in hand, even if your previous EAD has expired and USCIS approved the extension. Build at least a two-week buffer into any negotiated start date to account for mail delivery delays.
Frequently Asked Questions
Does my degree qualify me for the STEM OPT extension as a Machine Learning Scientist?
Your degree qualifies if its CIP code appears on the DHS STEM Designated Degree Program List. Computer science, statistics, mathematics, electrical engineering, and data science degrees typically qualify. Check your I-20 for your recorded CIP code and cross-reference it with the DHS list before your DSO submits your extension recommendation. Degrees in non-STEM fields do not qualify even if your job title is technical.
What E-Verify requirement applies to my Machine Learning Scientist employer?
Your employer must be enrolled in E-Verify before you begin working under STEM OPT authorization. Enrollment applies at the specific worksite level, not just the parent company, so verify that the office or lab where you'll actually work is enrolled. You can confirm enrollment through the E-Verify employer search tool. Companies that hire through staffing agencies must also meet additional placement requirements under STEM OPT rules.
What goes into the I-983 training plan for a Machine Learning Scientist role?
The I-983 must describe how your day-to-day work as a Machine Learning Scientist directly relates to your STEM degree, with specific learning objectives and measurable outcomes. For ML roles, this means documenting model development cycles, research methodologies, or production deployment goals rather than general job duties. Your DSO reviews and approves it, and your employer must report your progress to USCIS every six months. Vague plans are routinely sent back for revision.
How does cap-gap protection work if I'm a Machine Learning Scientist on STEM OPT?
If your employer files an H-1B petition on your behalf before your STEM OPT EAD expires, cap-gap automatically extends your work authorization and status through September 30 of that fiscal year. You can continue working as a Machine Learning Scientist without interruption during that period. If your H-1B petition is not selected in the lottery or is denied, your cap-gap protection ends and you must stop working immediately.
How do I find Machine Learning Scientist employers who support STEM OPT?
Search Migrate Mate for Machine Learning Scientist roles filtered to employers with active DOL Labor Condition Application filings. LCA history is a reliable indicator that a company has the compliance infrastructure to support STEM OPT, including E-Verify enrollment and willingness to engage in the I-983 process. Targeting these employers upfront avoids late-stage conversations where a company realizes it can't meet the federal requirements.