STEM OPT Machine Learning Jobs
Machine Learning roles in data science, NLP, and computer vision fall under STEM-designated CIP codes, making them eligible for the 24-month STEM OPT extension beyond your initial 12 months. Your employer must be enrolled in E-Verify and sign an I-983 training plan before your DSO can authorize the extension.
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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.
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Get Access To All JobsTips for Finding STEM OPT Authorization in Machine Learning
Verify your degree's CIP code eligibility
Check that your degree program's CIP code appears on the STEM Designated Degree Program List published by USCIS. Computer Science (11.07), Electrical Engineering (14.10), and Statistics (27.05) all qualify, but not every data-adjacent program does.
Confirm E-Verify enrollment before applying
Machine Learning roles at startups and research labs often lack E-Verify enrollment, which disqualifies them from STEM OPT. Search the E-Verify employer search tool by company name before submitting any application.
Target employers with active ML-specific LCA filings
Use Migrate Mate to filter for employers with verified Labor Condition Application filings under SOC codes like 15-2051 (Data Scientists) and 15-1252 (Software Developers). These companies have already navigated STEM OPT and understand the I-983 obligation.
Negotiate your I-983 training plan before signing
Your offer letter is not enough: USCIS requires a signed I-983 detailing your ML learning objectives, supervision structure, and how the role relates to your degree. Raise this document with HR during the offer stage, not after your start date.
Check prevailing wage against the OFLC Wage Search
Your employer must pay at least the DOL prevailing wage for your occupation and location. Run your job title and county through the OFLC Wage Search to catch underpaid offers early, since a below-wage role can jeopardize future H-1B visa sponsorship.
Build a portfolio aligned with O*NET ML task definitions
O*NET defines Machine Learning Engineers and Data Scientists by specific tasks: model training, feature engineering, and deployment pipelines. Structure your GitHub portfolio and resume around those task definitions so your application maps cleanly to the specialty occupation standard.
Frequently Asked Questions
Which STEM degrees qualify for the STEM OPT extension in Machine Learning roles?
Degrees in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, and Data Science typically qualify, provided their CIP code appears on the STEM Designated Degree Program List published by USCIS. A Machine Learning or AI-specific master's program qualifies if the CIP code is listed. Degrees in Business Analytics or Information Systems may not qualify, so confirm your CIP code with your DSO before targeting STEM OPT positions.
Does every Machine Learning employer need to be enrolled in E-Verify?
Yes, E-Verify enrollment is a hard requirement for STEM OPT. No employer exemptions exist regardless of company size, funding stage, or role seniority. Many early-stage AI startups and university spin-outs are not yet enrolled, so verify enrollment directly through the E-Verify employer search before accepting an offer. Migrate Mate filters for E-Verify-enrolled employers so you can focus on companies that are already eligible.
What goes into the I-983 training plan for a Machine Learning role?
The I-983 must describe your learning objectives in relation to your degree, identify your direct supervisor, outline how the ML work connects to your field of study, and include a schedule for self-evaluations every six months. For Machine Learning roles, training plans typically document objectives around model development, research methodologies, and software engineering practices. Your employer signs the form and your DSO endorses it before USCIS authorizes your EAD extension.
How does cap-gap protection apply if my employer files for H-1B during my STEM OPT period?
If your employer files a timely H-1B petition before your STEM OPT EAD expires and you are selected in the lottery, cap-gap automatically extends your work authorization through September 30 of that fiscal year. You can continue working as a Machine Learning engineer during this period without a new EAD. If your petition is not selected, your STEM OPT authorization continues until its original expiration date, assuming the petition was filed before that date.
How do I find Machine Learning jobs where employers already understand STEM OPT requirements?
Search Migrate Mate for Machine Learning roles filtered by E-Verify-enrolled employers with active LCA filing history under relevant SOC codes. Employers who have previously filed LCAs for data science or software engineering roles are more likely to have internal processes for onboarding STEM OPT students and completing the I-983 training plan without delays.