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Causal Inference Machine Learning Postdoctoral Jobs in Washington, DC

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

DeepSig, Inc

Machine Learning DSP Engineer

Arlington, VA · On-site

$164K - $192K/yr

We are seeking Full-time Machine Learning DSP Engineer who will help combine elements of software ... Utilize both Software and DSP techniques to optimize training and inference pipelines * Work ...

Leidos

Data Scientist

Springfield, VA · On-site

$116K - $210K/yr

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

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

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

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

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

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

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

TheIncLab

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

We are looking for a Senior Machine Learning Engineer to that will focus on researching, designing ... Experience deploying ML models into production (batch or real-time inference) Background in ...

TheIncLab

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Your Mission, Should You Choose to Accept As a Machine Learning Engineer, you will research ... Experience deploying ML models into production (batch or real-time inference) Background in ...

TheIncLab

Senior Machine Learning Engineer

Mclean, VA

$105K - $145K/yr

We are looking for a Senior Machine Learning Engineer to that will focus on researching, designing ... Experience deploying ML models into production (batch or real-time inference) Background in ...

Tetra Tech

Machine Learning Engineer - Remote

Vienna, VA · On-site

$114K - $138K/yr

... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Engineer high-quality features and maintain training/inference pipelines. Cloud and Platform ...

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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 Sep 15, 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:

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.

Senior Machine Learning Engineer

Washington, DC • Remote

Clearview AI, Inc.
Public Safety Statistics Centers and Offices • 11 - 50 employees
Learn more about Clearview AI, Inc.external

$107K - $146K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 20 days ago

Job description


Clearview AI is the leading provider of facial recognition technologies to US law enforcement, state, and federal agencies. Our mission is to help our users solve crimes and prevent financial fraud with the responsible use of our facial recognition software. Our company is a high-octane, fast growing startup looking to hire enthusiastic and intelligent team members to join our team. To learn more about us, and our revolutionary facial recognition technology, please visit www.clearview.ai.

Senior Machine Learning Engineer


Position Summary: We are hiring a highly technical individual contributor to push the limits of our computer vision and machine learning capabilities. This is a high-impact, hands-on role for a research-minded engineer who wants to build and ship models, not manage a team. Much of the work involves large-scale visual understanding, extracting structured signals from imagery and reasoning about the real-world context behind a photograph, but we care more about deep ML/CV ability than any one problem area and welcome strong generalists.

Responsibilities:
  • Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
  • Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
  • Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
  • Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
  • Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
  • Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
  • Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
  • Implement efficient, scalable data pipelines and inference infrastructure
  • Develop high-performance tooling in ML and data engineering
  • Additional duties and responsibilities as reasonably required by the employee's supervisor or CEO
Requirements:
  • Experience building, training, evaluating, and deploying ML models in production
  • Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
  • Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
  • Ability to solve open-ended problems and quickly learn new domains
  • Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraints
  • BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience

Nice to have:
  • Experience inferring structured, real-world attributes from images
  • Experience training models on large-scale, real-world image datasets
  • Familiarity with vision-language models (VLMs)
  • Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
  • Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
  • Experience in an ML role at a growth-stage startup
  • Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)
  • Medical, Dental, Vision, STD and LTD Plans
  • FSA - Medical and Dependent Care
  • EAP and wellness programs
  • 13 Paid Holidays
  • Unlimited PTO
  • Flexible work environment - 100% remote
  • 401(k) plan

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