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Machine Learning Researcher Jobs in New Mexico (NOW HIRING)

HS Research Tech 2

Albuquerque, NM · On-site

$17.50 - $24/hr

Experience or familiarity with artificial intelligence and machine learning methods is highly desirable. Prior research experience in a laboratory, academic, or applied research setting is preferred.

$135K - $180K/yr

In order to technically lead a robotics and machine learning project Teledyne FLIR Defense is looking for a motivated Sr. Research Engineer who can serve as a Principal Investigator. Primary Duties ...

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Machine Learning Researcher information

See New Mexico salary details

$29.1K

$109.6K

$159.4K

How much do machine learning researcher jobs pay per year?

As of Jun 13, 2026, the average yearly pay for machine learning researcher in New Mexico is $109,604.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,900.00 and $149,200.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Researchers face when transitioning from academic research to industry roles?

Machine Learning Researchers often find that transitioning to industry involves adapting to faster project timelines, collaborative workflows, and a focus on scalable, real-world solutions rather than theoretical advances alone. In industry, you'll likely work closely with cross-functional teams, such as software engineers and product managers, to ensure models are both practical and maintainable. Balancing innovation with business objectives, handling production constraints, and communicating complex findings to non-technical stakeholders are some of the key challenges you may encounter.

What are the key skills and qualifications needed to thrive as a Machine Learning Researcher, and why are they important?

To thrive as a Machine Learning Researcher, you need deep expertise in mathematics, statistics, programming (typically Python), and a strong academic background in computer science or related fields. Familiarity with frameworks like TensorFlow or PyTorch and experience with tools for data analysis and model development are standard, often supported by advanced degrees or relevant certifications. Critical thinking, creativity, and effective communication are vital soft skills for developing novel solutions and collaborating across interdisciplinary teams. These skills enable researchers to design innovative algorithms, validate models rigorously, and contribute impactful advancements in the field.

What is the difference between Machine Learning Researcher vs Data Scientist?

AspectMachine Learning ResearcherData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; research experienceDegree in CS, statistics, or related; strong analytical skills
Work EnvironmentResearch labs, academia, R&D departmentsBusiness environments, tech companies, consulting
Employer & Industry UsageUniversities, research institutions, tech firmsCorporations, startups, finance, healthcare
Common Search & ComparisonFocus on theoretical ML advancementsFocus on data analysis & business insights

While both roles involve working with data and algorithms, Machine Learning Researchers primarily focus on developing new algorithms and advancing ML theory, often in research or academic settings. Data Scientists apply these techniques to analyze data, generate insights, and support business decisions in industry environments.

What does a Machine Learning Researcher do?

A Machine Learning Researcher designs, develops, and tests algorithms and models that allow computers to learn from and make decisions based on data. They often work on advancing the field by exploring new methods, improving existing algorithms, and publishing their findings. These researchers collaborate with engineers and data scientists to apply their research to practical problems in areas like computer vision, natural language processing, and robotics. Their work typically involves a combination of mathematics, statistics, programming, and experimentation.
What are popular job titles related to Machine Learning Researcher jobs in New Mexico? For Machine Learning Researcher jobs in New Mexico, the most frequently searched job titles are:
What job categories do people searching Machine Learning Researcher jobs in New Mexico look for? The top searched job categories for Machine Learning Researcher jobs in New Mexico are:
What cities in New Mexico are hiring for Machine Learning Researcher jobs? Cities in New Mexico with the most Machine Learning Researcher job openings:
Infographic showing various Machine Learning Researcher job openings in New Mexico as of June 2026, with employment types broken down into 97% Full Time, 2% Part Time, and 1% Temporary. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $109,604 per year, or $52.7 per hour.
Data-driven and Machine Learning Postdoctoral Research Associate

Data-driven and Machine Learning Postdoctoral Research Associate

Los Alamos National Laboratory

Los Alamos, NM • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 17 days ago


Los Alamos National Laboratory rating

9.2

Company rating: 9.2 out of 10

Based on 32 frontline employees who took The Breakroom Quiz

7th of 103 rated laboratories


Job description

Description
Job Title Data-driven and Machine Learning Postdoctoral Research Associate
Location Los Alamos, NM, US
Organization Name Computational Physics and Methods (CAI-2)
Minimum Salary
Maximum Salary
What You Will Do
The Computational and Physics Methods Group (CAI-2) in the Computing and Artificial Intelligence Division at Los Alamos National Laboratory is seeking a skilled and driven researcher for a postdoctoral position at the intersection of applied mathematics, data-driven modeling of dynamical systems, and machine learning.
The successful candidate will join a multidisciplinary team of mathematicians, physicists, and machine learning researchers advancing AI-enabled modeling of complex dynamical systems. Leveraging transfer operator theory (e.g., Koopman and Perron-Frobenius methods), the postdoc will develop novel learning architectures that both respect physical constraints and help discover underlying structure from data. Core activities will span method development, theoretical analysis, and empirical validation at scale on benchmark and mission-relevant datasets. The position offers exposure to multiple application domains (e.g., wildfire, ocean, and space weather), as well as opportunities for cross-disciplinary collaboration, scientific workshop organization, and conference participation.
What You Need
Minimum Job Requirements:
  • Experience in data-driven and/or ML methods for dynamical systems, as evidenced through a strong scientific record of peer-reviewed publications and presentations.
  • Fundamental understanding of the Koopman and/or Perron-Frobenius Operators.
  • Excellent scientific programming skills with demonstrated, hands-on experience (beyond online courses/certifications) using modern ML libraries and tools-e.g., PyTorch and/or JAX-as well as high-level languages such as Python (including NumPy/SciPy).
  • Strong mathematical training in at least one relevant field (e.g., functional analysis/operator theory, probability/stochastic processes, numerical analysis/scientific computing, and/or optimization/ML theory).
  • Ability to work both independently and collaboratively in an interdisciplinary environment.
  • Ability to communicate technical results clearly in writing and presentations.
  • Demonstrated creativity and interest in developing new research directions and original methodologies.

Education/Experience : PhD in Applied Mathematics, Computational or Statistical Physics, Applied Statistics, Computer Science, or a related field completed within the last 5 years or to be completed soon.
Desired Qualifications:
  • Prior research experience directly involving the Koopman and/or Perron-Frobenius operators.
  • Prior research experience developing and/or implementing neural operators.
  • Strong background in functional analysis/operator theory, including spectral theory, reproducing kernel Hilbert space methods, and the approximation of infinite-dimensional systems by finite-dimensional models.
  • Experience with probabilistic modeling and uncertainty quantification (e.g., Bayesian deep learning, generative models, variational inference, ensembles, probabilistic scoring rules).
  • Experience developing novel neural network architectures (e.g., customized loss functions, complex network topologies, constrained or structure-preserving architectures).
  • Experience working with large numerical simulations or high-dimensional datasets and familiarity with high-performance computing environments (e.g., clusters, GPUs, job schedulers).

Work Location : The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the discretion of management.
Note to Applicants:
Due to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China-including the special administrative regions of Hong Kong and Macau-as well as citizens of the Islamic Republic of Iran, the Democratic People's Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents ("green card" holders) are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National Laboratory.
For full consideration please include:
• A comprehensive CV with publication list
• A cover letter describing your qualifications and how you meet the job requirements
• Contact information for at least three professional references.
For questions about this position contact: Derek DeSantis (ddesantis@lanl.gov) or Yen Ting Lin (yentingl@lanl.gov).
For more information, visit LANL career page: https://www.lanl.gov/careers/index.php .
Outstanding candidates may be considered for a Director's Postdoc Fellowship. For more information about the Postdoc Program, go to: https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php
Where You Will Work
Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. Our generous benefits package includes:
  • PPO or High Deductible medical insurance with the same large nationwide network
  • Dental and vision insurance
  • Free basic life and disability insurance
  • Paid childbirth and parental leave
  • Award-winning 401(k) (6% matching plus 3.5% annually)
  • Learning opportunities and tuition assistance
  • Flexible schedules and time off (PTO and holidays)
  • Onsite gyms and wellness programs
  • Extensive relocation packages (outside a 50 mile radius)

Additional Details
Directive 206.2 - Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2 . Please note that this requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.
No Clearance: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.
Internal Applicants: Regular appointment employees who have served the required period of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the required period of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader. Please refer to Policy Policy P701 for applicant eligibility requirements.
Equal Opportunity: Los Alamos National Laboratory is an equal opportunity employer. All employment practices are based on qualification and merit, without regard to protected categories such as race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal, state, and local laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to applyhelp@lanl.gov or call (505)-664-6947.
Employment Status Full Time
Appointment Type Postdoc
Postdoc
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