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Causal Inference Machine Learning Postdoctoral Jobs in Washington

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site

$140K - $150K/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 ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/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 ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search ...

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Causal Inference Machine Learning Postdoctoral information

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, and why are they important?

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.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Washington? For Causal Inference Machine Learning Postdoctoral jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Washington look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Washington are:
What cities in Washington are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Washington with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Washington as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Director, AI Engineering (Data Science)

Director, AI Engineering (Data Science)

Blend360

Columbia, MD • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 24 days ago

Job description

Company Description
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com.
Job Description
We are seeking a visionary and execution-oriented Director of AI Engineering to join our team. In this senior, client facing role, you will own the full lifecycle of AI model development, setting technical strategy and ensuring that cutting-edge machine learning solutions move from concept to production with business impact at their core.
With roots in data science and hands-on expertise in custom transformer architecture, you will bring both the credibility to lead technical teams. You will operate at the intersection of stakeholder management and deep technical execution and you should be equally comfortable presenting to a senior audience and reviewing model architecture with your team.
This is a high-impact, high-autonomyy role with significant organizational influence. You will define the AI roadmap, establish engineering best practices, and champion a culture of rigorous, reproducible, and responsible machine learning.
Strategic Leadership & Team Management
  • Define technical investments with business objectives
  • Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers-setting performance expectations and a high-performance culture.
  • Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact.
  • Establish engineering standards, code review practices, and model governance frameworks across the AI org.

Custom Transformer Architecture & Model Development
  • Serve as the technical authority on deep learning architecture-personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction.
  • Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom.
  • Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance.
  • Champion reproducible experimentation and architectural decision documentation across the team.

Data Science & Applied Analytics
  • Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment.
  • Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks.
  • Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs.

Client & Executive Engagement
  • Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives.
  • Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence.
  • Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice.

MLOps, Infrastructure & Governance
  • Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
  • Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC).
  • Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements.

Qualifications
  • Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a closely related quantitative field; Ph.D. strongly preferred.
  • 10+ years of progressive experience in data science and machine learning, with at least 3-5 years in a people management or technical leadership role (Director, Sr. Manager, or Principal Engineer level).
  • Proven track record of leading high-performing AI/ML engineering teams in a fast-paced, client-facing or product environment.
  • Deep, hands-on expertise designing and training custom transformer architectures from scratch-not only fine-tuning pre-built checkpoints, but architecting novel attention mechanisms, embedding strategies, and model topologies.
  • Strong applied data science foundation: feature engineering, statistical modeling, causal inference, and experimental design across large-scale datasets.
  • Proficiency in Python and core ML/DL libraries: PyTorch (preferred), TensorFlow, HuggingFace Transformers, scikit-learn, XGBoost/LightGBM.
  • Direct experience with industry datasets in marketing & media (DSP/DMP logs, ad impression data, attribution pipelines, MMM) OR telecommunications (CDRs, network KPIs, subscriber behavior, churn datasets).
  • Command of SQL and large-scale data platforms: Spark, BigQuery, Snowflake, or Databricks.
  • Experience owning end-to-end MLOps: cloud deployment (SageMaker, Vertex AI, or Azure ML), monitoring, CI/CD for ML, and model governance.
  • Exceptional executive communication skills-able to translate complex model behavior into business language for C-suite and client audiences.

PREFERRED QUALIFICATIONS
  • Professional services experience across multiple client engagements or business units
  • Background in privacy-preserving ML: federated learning, differential privacy, or synthetic data generation-especially relevant in post-cookie marketing environments.
  • Knowledge of graph neural networks (GNNs) for social graph or network topology analysis in telecom contexts.
  • Published research or conference contributions (NeurIPS, ICML, KDD, RecSys, or industry equivalents) related to applied transformers, tabular deep learning, or domain-specific AI.
  • Experience with real-time inference and streaming ML pipelines (Kafka, Flink, or similar).
  • Demonstrated ability to build strategic partnerships with external clients, contributing to revenue growth or account expansion through technical leadership.
  • Deep experience with openai focused on embeddings
  • Experience building custom transformer models

Additional Information
The starting pay range for this role is $180,000 - $240,000. Actual compensation within the range will be dependent on several factors including but not limited to relevant experience, skills, certifications, training, and location. It is not typical for an individual to be hired at or near the top of the range and determining factors for compensation are considered for each individual circumstance. BLEND360 also offers a competitive benefits program to meet the health and financial well-being of our team and their families. You can look forward to a range of benefits including medical, dental, vision, 401K, PTO, paid holidays, commuter benefits, spending accounts, life insurance, disability coverage, and EAPs.

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