Executive Computer Vision Postdoc Jobs in California

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. 

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

We have an opening for a highly qualified and motivated Postdoctoral Researcher to join the National Ignition Facility (NIF) Automated Alignment team. This team develops, sustains, and improves the operational software that automatically aligns NIF laser systems prior to experiments. The role is primarily focused on computer vision and image processing, with software engineering and machine learning serving as important secondary skill sets to improve robustness, performance, and maintainability.

You will work closely with multidisciplinary teams of scientists and engineers working on innovative solutions that improve performance, supporting NIF Operations, maintaining and improving legacy capabilities, and analyzing and troubleshooting off-normal conditions in an operational environment. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home one or more days per week, when consistent with team and operational needs.

You will

• Research, design, develop, test, and maintain computer vision and image processing algorithms used for automated alignment of high energy laser systems.
• Develop and sustain production software that supports NIF Operations, including maintaining and improving legacy codebases.
• Analyze and troubleshoot off-normal system behavior, identify root causes, and implement corrective actions in collaboration with domain experts.
• Apply modern machine learning techniques where appropriate to improve robustness of image processing, estimation, or classification workflows.
• Follow production-level software engineering practices, including:
  • participation in technical requirements and design reviews
  • code reviews and adherence to coding standards
  • configuration management and traceability
  • continuous integration, automated testing, and quality practices appropriate for operational software
• Collaborate with NIF Alignment and Laser stakeholders to integrate algorithms into operational workflows and validate performance against mission needs.
• Document software, algorithms, testing approaches, and operational procedures to support long-term sustainment and knowledge transfer.
• Pursue research and publication activities that complement operational deliverables and advance computer vision and automation methods.
• Present results at internal reviews, seminars, and scientific meetings, and publish in peer-reviewed venues as appropriate.
• Perform other duties as needed.

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
• PhD in computer science, engineering, applied mathematics, physics, or a related field, with emphasis in computer vision, image processing, machine learning, software engineering, or a related area.
• Fundamental knowledge and/or experience in computer vision and image processing, including experience developing algorithms for robust, real-world data.
• Fundamental programming skills, with emphasis on Python and scientific computing workflows (for example, NumPy) or another modern stack. Ability to learn IDL and Matlab as needed and contribute to a gradual migration toward Python based systems.
• Demonstrated ability to develop, deliver, and sustain production-quality software using disciplined engineering practices (for example, version control, code review, testing, and documentation).
• Ability to develop independent research projects as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.

Qualifications We Desire

• Experience applying modern machine learning methods to improve robustness of computer vision pipelines.
• Experience with software engineering best practices in operational environments (for example, CI systems, automated testing, coding standards, configuration management, structured reviews).
• Experience maintaining and improving legacy codebases and implementing incremental modernization without disrupting operations.
• Familiarity with optical, laser, metrology, alignment, controls, or automation systems.
• Experience debugging complex systems that combine algorithms, software, data, and integration with hardware or operational processes.
• Ability to balance research objectives with operational deliverables and stakeholder needs.

Pay Range

$122,028 - $143,328 Annually

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

Additional Information

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2026 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance

None required.  However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  

National Defense Authorization Act (NDAA)

The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities.  The restrictions of NDAA Section 3112 apply to this position.  To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

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Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory.  If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request. 

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.