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Causal Inference Machine Learning Postdoctoral Jobs in Washington, DC

Lynker Corporation

Machine Learning Engineer

College Park, MD · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference. Qualifications The Machine Learning Engineer selected should have the ...

Lynker Corporation

Machine Learning Engineer

College Park, MD · On-site +1

$95K - $195K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference. Qualifications The Machine Learning Engineer selected should have the ...

Jobtailor

Data Scientist

Washington, DC · On-site

$110 - $170/hr

Select statistical and machine learning methods appropriate to the question and evidence * Conduct ... Develop forecasting, classification, risk, anomaly-detection, segmentation, causal, simulation, or ...

Clearview AI

Senior Machine Learning Engineer

Washington, DC · On-site +1

$180K - $250K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Senior Machine Learning Engineer Department: Engineering Employment Type: Full Time Location ... Optimize models for inference latency and throughput using techniques like distillation ...

Leidos

Data Scientist

Springfield, VA · On-site

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

Leidos

Data Scientist

Springfield, VA

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

Leidos

Data Scientist

Springfield, VA · On-site

$116K - $210K/yr

  • Medical

  • Retirement

  • PTO

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

Leidos

Data Scientist

Springfield, VA

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

Carnegie Mellon University

Data Scientist

Arlington, VA

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

Leidos

Data Scientist

Springfield, VA · On-site

$116K - $210K/yr

  • Medical

  • Retirement

  • PTO

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

Clearview AI, Inc.

Senior Machine Learning Engineer

Washington, DC · Remote

$107K - $146K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Senior Machine Learning Engineer Position Summary: We are hiring a highly technical individual ... Optimize models for inference latency and throughput using techniques like distillation ...

Leidos

Data Scientist

Springfield, VA · On-site

Statistical modeling and inference * Machine learning and artificial intelligence * Predictive analytics and forecasting * Data mining and pattern analysis * Feature engineering and model evaluation

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Washington, DC

Causal Inference Machine Learning Postdoctoral information

See Washington, DC salary details

$40.2K

$61.4K

$69.1K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 20, 2026, the average yearly pay for causal inference machine learning postdoctoral in Washington, DC is $61,413.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,600.00 and $64,000.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Washington, DC?

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

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Washington, DC look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Washington, DC are:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Washington, DC as of June 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $61,413 per year, or $29.5 per hour.

Machine Learning Engineer

Lynker Corporation

College Park, MD • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 6 days ago

Job description

Overview

Lynker is seeking a talented and experienced Machine Learning Engineer to support the Environmental Modeling Center (EMC) within the National Centers for Environmental Prediction (NCEP). The primary objective of this role is to assist in the development of ML based systems that predict the current weather conditions everywhere given sparse observation data (this process is known as Data Assimilation [DA]). . These systems will complement existing physics-based systems and be tested as independent prototypes, running alongside traditional DA workflows. The position is located at the NOAA Center for Weather and Climate Prediction (NCWCP) in College Park, MD.

Responsibilities

Duties of the Machine Learning Engineer will include the following:

The Machine Learning Engineer will perform their job duties to a high standard, working both independently and collaboratively.The core responsibility is to assist in the development, implementation, testing, and evaluation of an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system in support of  NOAA's National Blend of Models (NBM). The AI-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields.. Because these fields serve as the foundation for gridded forecasts issued by the National Weather Service, this system will directly contribute to improved forecast quality.

The successful Machine Learning Engineer will work on the following scientific and engineering tasks:

  • Conduct a comprehensive review of state-of-the-art AI-based data assimilation and end-to-end weather forecasting methodologies, systems, and frameworks. Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI-RTMA approach. 
  • Collaborate with NOAA's NBM team and key stakeholders to define product requirements for AI-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition. 
  • Design, implement, and maintain robust data pipelines to support AI-RTMA training, validation, testing, and evaluation. This includes collecting, formatting, quality-controlling, and integrating diverse observational datasets (e.g., conventional observations, satellite, radar, and other sources), as well as preparing model inputs, targets, metadata, and training/validation splits. 
  • Develop, train, rigorously test, and deploy a fully functional AI-RTMA system based on selected AI frameworks or architectures. 
  • Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference.
Qualifications

The Machine Learning Engineer selected should have the following:

  • Experience developing, training and deploying AI-based systems applied to geophysical systems.
  • Experience with common AI frameworks such as PyTorch, TensorFlow.
  • Experience working with earth observation data, including conventional observations, satellite, radar. 
  • Excellent Python programming skills.
  • Practical experience utilizing High Performance Computers (HPCs) and GPUs.
  • Proven experience working in a UNIX environment with advanced scripting languages.
  • Good communication skills, both oral and written, in English.

The Ideal Machine Learning Engineer will have the following:

  • In-depth knowledge of data assimilation techniques (observation forward modeling, quality control, variational-based and/or ensemble methods).
  • Strong foundation in the physical, statistical and mathematical basis of geophysical modeling (atmospheric and/or environmental).
  • Experience with cloud platforms and use of IDEs for development.
  • Experience with cloud-native data formats such as Zarr, Parquet.
  • Experience with compiled languages.
  • Comfort using agentic AI tools to accelerate development.
  • Experience executing numerical models on HPC platforms using parallelization frameworks and job scheduling systems.
  • Familiarity with coupled earth system models.
  • Knowledge of modern software engineering practices (requirements gathering, design, prototyping, version control, integration, testing, and documentation).
  • Prior experience in model testing, evaluation, or knowledge of verification principles.

About Lynker

Lynker is a growing, employee owned business, specializing in professional, scientific and technical services. Our continually expanding team combines scientific expertise with mature, results-driven processes and tools to achieve technically sound, cost effective solutions in hydrology/water sciences, geospatial analysis, information technology, resource management, conservation, and management and business process improvement.

We focus on putting the right people in the right place to be effective. And having the right people is critical for success. Our streamlined organization enables and empowers our talented professionals to tackle our customers' scientific and technical priorities - creatively and effectively.

Lynker offers a team-oriented work environment, and the opportunity to work in a culture of exceptionally skilled professionals who embrace sound science and creative solutions. Lynker's benefits include the following:

  • Comprehensive healthcare for the employee at no monthly cost
  • Healthcare benefit covers medical, prescription drug, dental, and vision
  • Personal Time Off (PTO) Policy plus paid holidays
  • Highly competitive compensation plan regularly calibrated against industry and location benchmarks
  • 401(k) retirement plan with company-matching
  • Employee Stock Ownership Plan (ESOP) - we're all company owners!
  • Flexible spending accounts
  • Employee assistance program (EAP)
  • Short- and long-term disability insurance
  • Life and accident insurance
  • Tuition assistance/Training/Workforce improvement reimbursement per year
  • Spot bonuses for exceptional performance
  • Annual Employee Recognition Awards with bonuses
  • Employee Referral Program
  • Free centralized, self-directed Learning Management System to learn at your own pace
  • Personalized career growth plans for every employee

Lynker is an E-Verify employer.

Lynker is an equal opportunity employer and makes all employment decisions based on merit, qualifications, and business needs. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other legally protected status under federal, state, or local laws.

 Fraud Alert: Recruitment Scam Warning: Lynker has been made aware of fraudulent individuals posing as Lynker recruiters and offering fake job opportunities. All legitimate Lynker job postings are listed on our official careers page. Communication from Lynker recruiters will come from an official @lynker.com email address.

    Employment Type: FULL_TIME

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