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Machine Learning Computational Chemistry Jobs in Washington

... Machine Learning Engineer for HPC, Computational Research Engineer, etc. DEGREE (Level Desired ... Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial ...

... Machine Learning Engineer for HPC, Computational Research Engineer, etc. DEGREE (Level Desired ... Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial ...

... Machine Learning Engineer for HPC, Computational Research Engineer, etc. DEGREE (Level Desired ... Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial ...

... Machine Learning Engineer for HPC, Computational Research Engineer, etc. DEGREE (Level Desired ... Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial ...

... Machine Learning Engineer for HPC, Computational Research Engineer, etc. DEGREE (Level Desired ... Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial ...

Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic ...

Showing results 41-60

Machine Learning Computational Chemistry information

See Washington salary details

$26.3K

$122.9K

$227.1K

How much do machine learning computational chemistry jobs pay per year?

As of Sep 11, 2026, the average yearly pay for machine learning computational chemistry in Washington is $122,901.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,672.00 and $165,881.00 per year, depending on experience, location, and employer.

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

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

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

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

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

What cities in Washington are hiring for Machine Learning Computational Chemistry jobs?

Cities in Washington with the most Machine Learning Computational Chemistry job openings:

Computational Economics Expert/Sr. Computational Economics Expert- (Contractual) - ITDDP

Washington, DC • On-site

International Monetary Fund
International Trade Financing • 1 - 5K employees

$104K - $131K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Work for the IMF. Work for the World.

Under the direction of the Section Chief (Data Science Section) in the IT Department at the IMF, the Computational Economics Expert provides Fund-wide services on Computational Economics using computer-based economic, econometric and machine learning modeling for the solution of economic problems related to the Fund's business capabilities of surveillance, lending, and capacity development.


The role requires combinations of strong Computer Science and Economics skills to provide efficient and integrated solutions to the business, not only on the technological dimension, but also on the economic and quantitative modeling dimensions. Additionally, developing and delivering training on use of economic, econometric and machine learning modeling techniques and software, and advising on mathematical and high performance computational related problems are integral parts of the role.
This position requires advanced programming skills and knowledge of or experience with Economic and Econometric Modeling.


Major Duties and Responsibilities

  • Collaborates with economists, financial sector specialists, and other professionals from the line-of-business in the selection of the appropriate methods and data sets for economic and econometric modeling.
  • Develops and implements advanced economic and econometric models.
  • Undertakes research towards crafting solutions for challenges arising from economic and econometric modeling.
  • Analyzes requests, designs methodology and develops programs and modules for advanced economic and econometric models.
  • Researches, analyzes, and develops algorithms to improve the performance and extend the capabilities of economic and econometric models, including optimization for parallel computing environments and large-scale simulations, as well as improving I/O efficiency in high-throughput data workflows.
  • Writes computational and data processing programs using high-level programming languages such as Matlab, Python, R, etc., including development and maintenance of reusable internal libraries and packages, implementation of standardized data access and reporting frameworks, and application of automated testing, documentation, and code quality assurance practices.
  • Supports the operation and continuous improvement of shared computational environments and software stacks, including contributions to system configuration, package management practices, and governance of analytical tools and resources, as needed to enable reproducible and efficient research workflows.
  • Develops course materials and provides training on use of economic and econometric modeling techniques.
  • Follows up current academic research on computational economics, applied mathematics and econometrics.

Minimum Qualifications

Advanced degree in Computer Science, Economics, Engineering, Applied Mathematics, or relevant field plus a minimum of four (4) years of post-graduation professional experience, or a bachelor's degree plus a minimum of ten (10) years of post-graduation professional experience is required. Additionally, below required competencies are required for this role:


1. Economic and Econometric Modeling Expertise

  • Knowledge and experience with economic and econometric models including time series, cross-section, panel data econometrics, and macroeconomic models (DSGE, HANK, ABM).
  • Proficiency in quantitative modeling, statistical estimation methods (maximum likelihood, method of moments, Bayesian inference, VAR), and use of econometric/statistical software (EViews, Stata, Matlab, Julia).
  • Ability to develop problem definitions, models, and constraints from informal requirements, managing ambiguity and competing objectives.

2. Mathematical, Numerical, and Optimization Methods

  • Expert knowledge of numerical methods, linear algebra, large-scale mathematical programming, and algorithm development.
  • Strong understanding of optimization techniques including linear, nonlinear, dynamic programming, simulation-based optimization, stochastic programming, robust optimization, and approximate dynamic programming.
  • Familiarity with computational complexity theory and applied/theoretical statistical learning.

3. Advanced Programming and Computational Skills

  • Advanced programming skills in scientific computing and data science languages such as Matlab, Python, and R.
  • Extensive experience with distributed and parallel computing.
  • Knowledge of machine learning algorithms, including deep learning.
  • Understanding of personal computer architecture and memory organization.
  • Ability to follow current academic research in computational economics, applied mathematics, and statistics.
  • Strong oral and written communication skills and ability to convey higher level technical concepts to non-experts.

This is a one-year contractual appointment. Contractual appointments at the IMF are renewable for up to four years of cumulative contractual service, pending incumbent's performance, budget availability, and continuous business need.

Department:

ITDDPDS Information Technology Department Data Platform Division Data Science Section

Hiring For:

A11, A12

The IMF is guided by the principle that the employment, classification, promotion, and assignment of staff shall be made without discrimination against any person. We welcome requests for reasonable accommodations for disabilities during the selection process. Information on how to request accommodations will be provided during the application process.