STEM OPT Research Scientist Jobs
Research Scientist roles qualify for the STEM OPT 24-month extension when your employer is enrolled in E-Verify and your degree aligns with a STEM-designated CIP code. With up to 36 months of total OPT work authorization, you have a realistic runway to contribute to long-term research projects and pursue H-1B visa sponsorship.
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We are seeking a Research Scientist to join the SAF Lab. In this role, you will develop the core Control Barrier Function (CBF) theory and algorithms that form the mathematical foundation of the universal safety layer. Key to this process is a feedback loop between theory and practice: developing theory that is deployed on next generation robots and using experimental evaluation to drive new theory. This will enable you to push the boundaries of CBF theory: layered safety filters and trade-offs between robustness and optimality. A key challenge will be to understand the interplay with CBF theory and learned control policies, constructing safety filters that internalize learned policies and utilizing CBFs in learning to internalize safety. You will work with the inventor of control barrier functions and a team contributing directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet.
Key job responsibilities
- Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope highly dynamic robots
- Frame safety filtering within complex layered architectures involving learning-based components, including VLAs, RL-based locomotion and whole-body controllers
- Design multi-layer CBF based safety filters, including decision making layers, MPC, and real-time nonlinear feedback control elements
- Formalize the interplay between models used in the CBF safety filter and the full order dynamics of the robotic systems, establishing formal guarantees even if the full order system dynamics is not known and contains learning-based elements
- Understand the role of perception and semantic representations in the synthesis of CBFs, and the interplay between CBFs
- Characterize the trade-offs between optimal safety and robustness to sensor noise, perception error, actuator and sensor failure
- Address the theory-to-practice gap by developing CBF methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency
- Implement real-time optimization solvers (e.g., QP-based safety filters) that execute within the tight timing budgets of safety-critical control loops
- Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins
- Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues
- Collaborate with perception, planning, locomotion, and manipulation teams to ensure CBF formulations accommodate the needs of upstream and downstream systems
- Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots)
A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
- Medical, Dental, and Vision Coverage
- Maternity and Parental Leave Options
- Paid Time Off (PTO)
- 401(k) Plan
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
About the team
Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin Amazon's path to millions of robots operating alongside people.
BASIC QUALIFICATIONS
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Deep expertise in Control Barrier Functions, including theoretical foundations and practical implementation
- Experience formulating and solving optimization-based controllers (QPs, SOCPs) for real-time safety filtering
- Strong mathematical background in dynamical systems theory, nonlinear control, and formal verification or reachability analysis
- Proficiency in C++ and Python with experience implementing control algorithms for real-time systems
- Experience validating safety-critical algorithms on physical robotic hardware (not simulation-only)
- Publication record at relevant venues (e.g., CDC, ACC, L-CSS, ICRA, RSS, RAL, Automatica, TAC, TRO)
PREFERRED QUALIFICATIONS
- Understanding of control systems engineering, with a specific focus on layered architecture used in robotic systems (high level planning, mid-level trajectory generation and low-level feedback control)
- Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches
- Experience with robust or adaptive CBF methods that account for parametric uncertainty or unmodeled dynamics
- Experience with sum-of-squares (SOS) programming, Lyapunov function synthesis, or other computational tools for verifying set invariance
- Familiarity with real-time embedded systems and the constraints of deploying optimization-based controllers on safety-rated hardware
- Experience applying CBFs to multi-agent systems or high-dimensional robotic platforms (manipulators, legged robots)
- Exposure to learning-based approaches for CBF synthesis (e.g., neural CBFs, data-driven barrier functions) and the integration of CBFs into learning (e.g., CBF-RL)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
LOCATION
USA, CA, PASADENA - 136,000.00 - 184,000.00 USD annually
DESCRIPTION
We are seeking a Research Scientist to join the SAF Lab. In this role, you will develop the core Control Barrier Function (CBF) theory and algorithms that form the mathematical foundation of the universal safety layer. Key to this process is a feedback loop between theory and practice: developing theory that is deployed on next generation robots and using experimental evaluation to drive new theory. This will enable you to push the boundaries of CBF theory: layered safety filters and trade-offs between robustness and optimality. A key challenge will be to understand the interplay with CBF theory and learned control policies, constructing safety filters that internalize learned policies and utilizing CBFs in learning to internalize safety. You will work with the inventor of control barrier functions and a team contributing directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet.
Key job responsibilities
- Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope highly dynamic robots
- Frame safety filtering within complex layered architectures involving learning-based components, including VLAs, RL-based locomotion and whole-body controllers
- Design multi-layer CBF based safety filters, including decision making layers, MPC, and real-time nonlinear feedback control elements
- Formalize the interplay between models used in the CBF safety filter and the full order dynamics of the robotic systems, establishing formal guarantees even if the full order system dynamics is not known and contains learning-based elements
- Understand the role of perception and semantic representations in the synthesis of CBFs, and the interplay between CBFs
- Characterize the trade-offs between optimal safety and robustness to sensor noise, perception error, actuator and sensor failure
- Address the theory-to-practice gap by developing CBF methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency
- Implement real-time optimization solvers (e.g., QP-based safety filters) that execute within the tight timing budgets of safety-critical control loops
- Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins
- Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues
- Collaborate with perception, planning, locomotion, and manipulation teams to ensure CBF formulations accommodate the needs of upstream and downstream systems
- Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots)
A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
- Medical, Dental, and Vision Coverage
- Maternity and Parental Leave Options
- Paid Time Off (PTO)
- 401(k) Plan
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
About the team
Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin Amazon's path to millions of robots operating alongside people.
BASIC QUALIFICATIONS
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Deep expertise in Control Barrier Functions, including theoretical foundations and practical implementation
- Experience formulating and solving optimization-based controllers (QPs, SOCPs) for real-time safety filtering
- Strong mathematical background in dynamical systems theory, nonlinear control, and formal verification or reachability analysis
- Proficiency in C++ and Python with experience implementing control algorithms for real-time systems
- Experience validating safety-critical algorithms on physical robotic hardware (not simulation-only)
- Publication record at relevant venues (e.g., CDC, ACC, L-CSS, ICRA, RSS, RAL, Automatica, TAC, TRO)
PREFERRED QUALIFICATIONS
- Understanding of control systems engineering, with a specific focus on layered architecture used in robotic systems (high level planning, mid-level trajectory generation and low-level feedback control)
- Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches
- Experience with robust or adaptive CBF methods that account for parametric uncertainty or unmodeled dynamics
- Experience with sum-of-squares (SOS) programming, Lyapunov function synthesis, or other computational tools for verifying set invariance
- Familiarity with real-time embedded systems and the constraints of deploying optimization-based controllers on safety-rated hardware
- Experience applying CBFs to multi-agent systems or high-dimensional robotic platforms (manipulators, legged robots)
- Exposure to learning-based approaches for CBF synthesis (e.g., neural CBFs, data-driven barrier functions) and the integration of CBFs into learning (e.g., CBF-RL)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
LOCATION
USA, CA, PASADENA - 136,000.00 - 184,000.00 USD annually
See all 1,118+ STEM OPT Research Scientist Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Research Scientist
Verify your CIP code before applying
Check that your degree's Classification of Instructional Programs code appears on the DHS STEM OPT designated degree list. Biology, chemistry, computer science, and engineering fields qualify, but interdisciplinary degrees sometimes carry non-STEM CIP codes that disqualify you.
Confirm E-Verify enrollment before accepting offers
Research institutions, national labs, and private R&D firms must be enrolled in E-Verify to hire STEM OPT students. Search the E-Verify employer search tool by company name before signing anything, enrollment status is not always disclosed upfront in job postings.
File your I-983 training plan before your start date
Your DSO must receive a completed I-983 signed by both you and your Research Scientist supervisor before your STEM OPT extension begins. Delays in supervisor sign-off at large institutions are common, so build in two weeks of buffer.
Use O*NET to match your role to STEM OPT criteria
Pull the Research Scientist occupation profile from O*NET and cross-reference the listed knowledge areas with your degree field. This documentation strengthens your I-983 training plan and helps your employer's HR team confirm the role qualifies.
Target employers with active H-1B filing history
Research Scientist roles often lead directly to H-1B sponsorship. Use Migrate Mate to filter employers by verified DOL Labor Condition Application filings in research and life sciences occupations, so you focus your applications on organizations that have already committed to sponsoring workers in your field.
Request employer cap-gap documentation immediately after lottery selection
If USCIS selects your H-1B petition in the annual lottery before your STEM OPT expires, cap-gap protection automatically extends your work authorization through September 30. Ask your employer's immigration team to provide written confirmation of your cap-gap status so you can continue working without interruption.
Frequently Asked Questions
Does a Research Scientist role qualify for the STEM OPT extension?
Yes, if your employer is enrolled in E-Verify and your degree carries a STEM-designated CIP code. Research Scientist positions in life sciences, physical sciences, computer science, and engineering routinely qualify. Your DSO confirms eligibility by matching your degree's CIP code against the DHS STEM designated degree list before recommending the extension to USCIS.
What goes into the I-983 training plan for a Research Scientist position?
Your I-983 must describe the specific research skills, laboratory techniques, or computational methods you'll develop on the job, how those learning goals connect to your STEM degree, and how your supervisor will evaluate your progress. Both you and your supervising researcher must sign it. USCIS can audit these plans, so vague descriptions like 'conduct experiments' are insufficient without linking tasks to degree-specific competencies.
How do I find Research Scientist employers who are enrolled in E-Verify?
E-Verify enrollment is mandatory for STEM OPT employers but isn't always listed in job postings. You can search the E-Verify employer database by organization name to confirm enrollment before applying. Migrate Mate also surfaces Research Scientist roles filtered for E-Verify compliance and active LCA filing history, saving you from applying to employers who can't legally hire STEM OPT students.
What happens to my STEM OPT authorization if my H-1B is selected in the lottery?
Cap-gap protection automatically extends your work authorization if your H-1B petition is filed before your STEM OPT expires and USCIS selects it in the lottery. Your authorization continues through September 30 of the fiscal year your H-1B takes effect. Research Scientist employers typically file H-1B petitions in April, so your STEM OPT must still be valid at that filing date for cap-gap to apply.
Can I switch Research Scientist employers while on STEM OPT?
Yes, but you must report the change to your DSO within 10 days and file a new I-983 training plan with the incoming employer before you start working there. The new employer must also be enrolled in E-Verify. Your authorization does not transfer automatically, and working for the new employer before your DSO processes the update creates a status violation.