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Machine Learning Researcher information

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$27.2K

$102.7K

$149.3K

How much do machine learning researcher jobs pay per year?

As of May 28, 2026, the average yearly pay for machine learning researcher in Tennessee is $102,653.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,800.00 and $139,800.00 per year, depending on experience, location, and employer.

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 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 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 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 are popular job titles related to Machine Learning Researcher jobs in Tennessee? For Machine Learning Researcher jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Machine Learning Researcher jobs in Tennessee look for? The top searched job categories for Machine Learning Researcher jobs in Tennessee are:
Machine Learning Researcher jobs near you
Infographic showing various Machine Learning Researcher job openings in Tennessee as of May 2026, with employment types broken down into 52% Full Time, 42% Part Time, 3% Contract, and 3% Nights. Highlights an 95% Physical, and 5% Remote job distribution, with an average salary of $102,653 per year, or $49.4 per hour.
Postdoctoral Research Associate - Data Scientist

Postdoctoral Research Associate - Data Scientist

Oak Ridge National Laboratory

Oak Ridge, TN • On-site

Full-time

Posted 7 days ago

Oak Ridge National Laboratory rating

9.3

Company rating: 9.3 out of 10

Based on 15 frontline employees who took The Breakroom Quiz

4th of 103 rated laboratories

Job description

Requisition Id 16485
Overview:
The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance computing (HPC), with an emphasis on developing intelligent systems that can accelerate large-scale scientific research on leadership-class supercomputers.
The successful candidate will contribute to research efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI directly into scientific workflows to enable autonomous, data-driven discovery in areas such as fusion energy, materials science, climate science, and nuclear energy.
As part of ORNL's interdisciplinary research environment, you will work alongside scientists, engineers, and computational researchers while leveraging world-class computing resources, including Frontier, the world's first exascale supercomputer. The role includes developing and advancing open-source software for large-scale hyperparameter optimization (HPO), neural architecture search (NAS), and Bayesian optimization on distributed HPC systems.
Research activities will address key challenges in AI for science, including surrogate modeling, uncertainty quantification, and multi-fidelity optimization for complex simulation workflows. This position offers an opportunity to contribute to cutting-edge AI and HPC research while supporting DOE's broader mission to advance scientific innovation through computational science.
The appointment length is 2 years with the possibility of extension, subject to performance and availability of funding.
Major Duties and Responsibilities:
  • Conduct research and development in scalable AI/ML methods for scientific computing and high-performance computing environments.
  • Develop and evaluate optimization techniques for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies.
  • Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications.
  • Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with Quantum computing.
  • Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows.
  • Contribute to the development and maintenance of open-source software, including testing, documentation, and user support activities.
  • Work closely with researchers and domain scientists to communicate results, define research directions, and support collaborative projects.
  • Publish research findings in peer-reviewed journals and present work at scientific workshops and conferences.
  • Design and implement scalable AI/ML optimization algorithms for hyperparameter optimization and neural architecture search, targeting scientific machine learning models running on leadership-class HPC systems.
  • Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success.

Basic Qualifications:
  • A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years.
  • An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences.
  • Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and hyperparameter optimization or AutoML techniques.
  • Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation).
  • Experience with HPC environments and parallel/distributed computing.
  • Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.

Preferred Qualifications:
  • Experience with multi-fidelity optimization, neural architecture search, or large-scale AutoML systems.
  • Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific applications.
  • Prior exposure to DOE workflows, national laboratory environments, or large-scale simulation codes.
  • Experience contributing to open-source scientific software projects.

#LI-DC1
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.

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