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Machine Learning Postdoc Jobs in Sunnyvale, CA (NOW HIRING)

Stanford University

Staff Scientist

Stanford, CA · On-site

... in machine learning and/or statistical methods with experience in developing new approaches • Training and research experience (preferably at postdoctoral level) in at least one of the following ...

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Machine Learning Postdoc information

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$25.1K

$130K

$244.7K

How much do machine learning postdoc jobs pay per year?

As of Aug 9, 2026, the average yearly pay for machine learning postdoc in Sunnyvale, CA is $130,021.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,152.00 and $179,068.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in a machine learning postdoc position?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What is a machine learning postdoc?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a machine learning postdoc?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What job categories do people searching Machine Learning Postdoc jobs in Sunnyvale, CA look for? The top searched job categories for Machine Learning Postdoc jobs in Sunnyvale, CA are:
Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Sunnyvale, CA as of July 2026, with employment types broken down into 1% As Needed, 64% Full Time, 33% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $130,021 per year, or $62.5 per hour.

Computational Materials Scientist - Postdoctoral Researcher

LLNL

Livermore, CA

$123K/yr

Full-time

Retirement

Re-posted 23 days ago

Job description

Company Description

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.

    Job Description

    We have an opening for a Postdoctoral Researcher to work in the field of computational materials science and actively participate in research dedicated to the discovery of new structural alloys for extreme environments. You will be involved in research to implement methods for materials design and process development. You will be a creative force in the integration and application of thermodynamic and kinetic models into an alloy design framework (including material properties models) that uses numerical optimization methods to rapidly screen for promising compositions over vast multi-component phase spaces. This position is in the Actinide and Lanthanide Science group within the Materials Science Division.

    In this role you will

    • Independently develop multicomponent thermodynamic and kinetic databases for inorganic (metal, oxide, carbide, hydride, etc.) systems.
    • Incorporate new Integrated Computational Materials Engineering (ICME) microstructure evolution models and resulting property predictions (ranging from analytical to machine learning surrogate models) in LLNL's Materials Acceleration Platform that is deployed on LLNL's High Performance Computing infrastructure.
    • Develop uncertainty quantification (UQ) and propagation methods into ICME approaches.
    • Interface with experimentalists in design of experiment and material development campaigns.
    • Work both independently and collaborate with others in a multidisciplinary team environment to accomplish program goals.
    • Publish research results in peer-reviewed scientific journals and present results at external conferences, seminars, and technical meetings.
    • Perform other duties as assigned.
    Qualifications
    • Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
    • PhD in materials science, metallurgy, condensed matter physics, applied math, or a closely related field.
    • Experience and knowledge in at least three of the following areas: CALPHAD, microstructure and property modeling, uncertainty quantification and propagation, ICME, alloy design, design of experiments.
    • Demonstrated ability to independently develop or make significant contributions to scientific research software.
    • Experience with commercial (Thermo-Calc, Pandat, or FactSage) or open source (PyCalphad, Thermochimica, or OpenCalphad) computational thermodynamics software to perform CALPHAD database development.
    • Experience in at least two of the following metallurgy topics: thermodynamics, phase stability, phase transformations, defect structures, solidification, or thermo-mechanical processing.
    • Proficient verbal and written communication skills as reflected in effective presentations at meetings and a demonstrated strong publication record.
    • Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.

    Qualifications We Desire

    • Experience with artificial intelligence (AI) and/or machine learning (ML) methods.
    • Experience with algorithms relevant for materials design and discovery (Bayesian Optimization, black-box optimization, gradient-based optimization).
    • Experience parameterizing or developing numerical methods for thermodynamic and/or kinetic modeling of phase transformations and microstructure evolution (e.g. CALPHAD, Kampmann-Wagner numerical method, phase field, cellular automata, Monte Carlo method, continuum models).
    • Direct experience with alloy synthesis, processing, and/or characterization; or extensive experience collaborating with experimental colleagues.

    Pay Range

    $123,048 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. 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

    All your information will be kept confidential according to EEO guidelines.

    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

    This position requires a Department of Energy (DOE) Q-level clearance.  If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing.  Q-level clearance requires U.S. citizenship. 

    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.

    How to identify fake job advertisements

    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.

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