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Freelance Machine Learning Compiler Engineer Jobs in Stockton, CA

Senior SAP Consultant

Modesto, CA · On-site

$150 - $190/hr

Experience with Agile or DevOps delivery methodologies. * Exposure to AI, SAP Business AI, SAP Joule, SAP BTP, machine learning, or enterprise automation initiatives. * SAP certifications are a plus.

Manufacturing Engineer 3 - NPI

Livermore, CA · On-site

$130K - $150K/yr

Health Spending Plans * Comprehensive Online Learning Center - houses thousands of training ... Knowledge of complex machining processes and equipment (mill, lathe, screw machine, etc.). * In ...

Engineer, Sustaining

Livermore, CA · On-site

$100K - $145K/yr

Health Spending Plans * Comprehensive Online Learning Center - houses thousands of training ... Knowledge of machining, assembly, welding, brazing, anodizing, cleaning, and other precision ...

Quality Engineer 3

Livermore, CA · On-site

$122K - $160K/yr

Health Spending Plans * Comprehensive Online Learning Center - houses thousands of training ... in the machining, semiconductor equipment, automotive or medical device sector * Ability to ...

Showing results 41-60

Freelance Machine Learning Compiler Engineer information

See Stockton, CA salary details

$15

$50

$139

How much do freelance machine learning compiler engineer jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for freelance machine learning compiler engineer in Stockton, CA is $50.25, according to ZipRecruiter salary data. Most workers in this role earn between $25.58 and $65.10 per hour, depending on experience, location, and employer.

What is the difference between Freelance Machine Learning Compiler Engineer vs Freelance Software Developer?

AspectFreelance Machine Learning Compiler EngineerFreelance Software Developer
Required SkillsMachine learning frameworks, compiler optimization, programming (C++, Python)General programming, software design, various languages
Work EnvironmentProject-based, remote, often technical teams in AI/ML industryVaried industries, remote or on-site, broad application areas
Industry UsageAI/ML companies, research labs, tech firmsTech, finance, healthcare, startups, enterprise

Freelance Machine Learning Compiler Engineers focus on optimizing ML models for deployment, requiring specialized knowledge in ML frameworks and compiler technology. Freelance Software Developers have broader roles across various industries, working on diverse software projects. Both roles are in high demand but differ in technical focus and industry application.

What are popular job titles related to Freelance Machine Learning Compiler Engineer jobs in Stockton, CA?

For Freelance Machine Learning Compiler Engineer jobs in Stockton, CA, the most frequently searched job titles are:

What job categories do people searching Freelance Machine Learning Compiler Engineer jobs in Stockton, CA look for?

The top searched job categories for Freelance Machine Learning Compiler Engineer jobs in Stockton, CA are:

What cities near Stockton, CA are hiring for Freelance Machine Learning Compiler Engineer jobs?

Cities near Stockton, CA with the most Freelance Machine Learning Compiler Engineer job openings:

Computational Materials Scientist - Postdoctoral Researcher

LLNL

Livermore, CA

$123K/yr

Full-time

Retirement

Re-posted 7 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.