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Postdoctoral Machine Learning Jobs in Washington

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How much do postdoctoral machine learning jobs pay per year?

As of Aug 23, 2026, the average yearly pay for postdoctoral machine learning in Washington is $66,848.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,500.00 and $75,300.00 per year, depending on experience, location, and employer.

What is a postdoctoral machine learning?

A Postdoctoral Machine Learning job is a research-focused position for individuals who have recently earned a Ph.D. in machine learning, artificial intelligence, or a related field. It typically involves conducting advanced research, publishing papers, collaborating with academic or industry partners, and developing novel algorithms or models. These roles are often hosted by universities, research institutes, or tech companies. The position helps researchers gain additional expertise and contribute to cutting-edge advancements before transitioning to faculty, industry, or independent research roles.

What are the typical daily responsibilities of a postdoctoral machine learning researcher?

A Postdoctoral Machine Learning researcher typically spends their day designing and implementing machine learning algorithms, analyzing experimental results, and preparing manuscripts for publication. They often collaborate with interdisciplinary teams of scientists and engineers, attend lab meetings, and contribute to grant writing or project proposals. Regular activities also include keeping up with recent scientific literature, mentoring graduate or undergraduate students, and presenting research findings at conferences or seminars. The blend of technical development and scientific communication makes each day dynamic and offers opportunities to influence both academia and industry.

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

To thrive as a Postdoctoral Machine Learning researcher, you need a strong background in machine learning theory, statistical analysis, and programming, typically supported by a Ph.D. in computer science, engineering, or a related quantitative field. Experience with Python, TensorFlow, PyTorch, and advanced data analytics tools is highly valued, as are relevant publications and experience with version control systems like Git. Strong problem-solving abilities, clear communication skills, and effective teamwork are crucial to excel in collaborative research settings. These skills and qualities are essential to drive innovative research, efficiently navigate complex datasets, and contribute to impactful scientific discoveries.

What are popular job titles related to Postdoctoral Machine Learning jobs in Washington?

For Postdoctoral Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Postdoctoral Machine Learning jobs in Washington look for?

The top searched job categories for Postdoctoral Machine Learning jobs in Washington are:

What cities in Washington are hiring for Postdoctoral Machine Learning jobs?

Cities in Washington with the most Postdoctoral Machine Learning job openings:

Postdoctoral Machine Learning jobs near you
Infographic showing various Postdoctoral Machine Learning job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 18% Part Time, 2% Temporary, and 5% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $66,848 per year, or $32.1 per hour.

NIST PREP Postdoc Associate in Interpretable DNA/RNA Ensemble Quantification

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$72K/yr

Full-time

Re-posted 3 days ago

Job description

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research Title:Interpretable DNA/RNA Ensemble Quantification (Molecular dynamics, machine learning, measurement analysis)
The work will entail: This position will focus on theory and computation to classify DNA and RNA conformational ensembles using secondary-structure-based distance metrics and clustering. A central goal is to build hierarchical, interpretable ensemble representations that connect simulation-derived clusters to experimental measurements/observables and statistical-physics interpretation (e.g., energetic barriers and kinetic pathways). Work includes developing and validating analysis algorithms, implementing reproducible research software, and collaborating with experimental and device-focused teams to connect theory outputs to measurement needs.
Key responsibilities will include but are not limited to:
  • Develop, test, and extend ensemble representations for DNA/RNA and relate these to experimental observables,
  • Implement and optimize secondary-structure distance metrics based on base-pair reorganization,
  • Build scalable clustering and model-selection for large molecular dynamics datasets,
  • Presenting results at internal and external meetings and conferences,
  • Develop well-documented, reproducible research software and publish results.

U.S. Citizen Preferred
Qualifications
  • A Ph.D. in physics, chemistry, biophysics, computational biology, applied mathematics, computer science, or a closely related field.
  • Demonstrated experience with biomolecular simulation and/or trajectory analysis (strong preference for nucleic acids: DNA/RNA).
  • Experience with coarse-grained nucleic-acid models, e.g., oxDNA/oxRNA or closely related coarse-grained frameworks.
  • Practical understanding of clustering/unsupervised learning and distance-metric design.
  • Strong scientific programming (Python preferred; Julia a plus) and ability to write maintainable, version-controlled code.
  • Background in statistics/statistical physics; ability to interpret ensembles in terms of kinetics and free-energy landscapes.
  • Strong written and oral communication skills and ability to collaborate in a multidisciplinary team; experience analyzing experimental data from single-molecule and ensemble techniques is a plus.

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0004514

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