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Causal Inference Machine Learning Postdoctoral Jobs in Seattle, WA

Docugami

Machine Learning / Data Scientist

Kirkland, WA · On-site

... causal inference, and other related disciplines * Programming skills and familiarity of modern ML frameworks * Research track demonstrated by top-level journal and conference publications * Strong ...

Constellation Space

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Optimize algorithms for low-latency inference on edge devices (spacecraft hardware). * Collaborate ... Proven experience deploying machine learning models into production. * Strong software engineering ...

Amazon

Applied Scientist, Pricing Science

Seattle, WA · On-site

The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon ... causal inference methods (synthetic DiD, generalized random forests, causal representation learning ...

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Causal Inference Machine Learning Postdoctoral information

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$40.4K

$61.7K

$69.4K

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

As of Jul 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Seattle, WA is $61,707.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,900.00 and $64,300.00 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, 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 Seattle, WA? For Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Seattle, WA are:
Causal Inference Machine Learning Postdoctoral jobs near you
ML Postdoc Researcher - LLMs & Generative AI

ML Postdoc Researcher - LLMs & Generative AI

Truveta

Seattle, WA • On-site

Full-time

Posted 11 days ago

Job description

Job Summary:
Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. They are seeking a highly motivated Machine Learning Postdoctoral Researcher to join their AI research team and contribute to innovative projects in Large Language Modeling and clinical data analysis.
Responsibilities:
• Collaborate with researchers and engineers to design, develop, and refine large language models and generative models for various applications.
• Utilize your expertise in machine learning and natural language processing to develop novel algorithms and methodologies for generative modeling tasks.
• Implement, train, and fine-tune LLM and GPT-like models on large-scale datasets to ensure optimal performance and accuracy.
• Stay up to date with the latest research advancements and techniques in the field of language modeling, generative modeling, and machine learning.
• Deliver the next generation of innovation in trustworthy healthcare.
Qualifications:
Required:
• Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, natural language processing (NLP), Large Language Models (LLMs), multi-modal foundation models, and generative AI
• Strong theoretical and practical background in NLP including experience with state-of-the-art architectures
• Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, etc.) and libraries commonly used in NLP and Generative AI
• Solid programming skills in Python and the ability to write clean, efficient, and well-documented code
• Excellent problem-solving and troubleshooting abilities, along with a strong analytical mindset and persistence in resolving problems
• Strong communication skills and the ability to work effectively in a collaborative research environment
Preferred:
• Experience with distributed parallel training, large-scale multi-modal foundation and generative models
• Familiarity with parameter-efficient tuning techniques, Reinforcement Learning from Human Feedback (RLHF), and prompt engineering techniques
• Familiarity with training multi-modal foundation models
• Familiarity with cloud-based infrastructure and experience deploying large-scale machine learning models in production environments
• A track record of publications and contributions to the machine learning and natural language processing communities
Company:
Truveta is a healthcare data platform that provides EHR data for scientific research. Founded in 2020, the company is headquartered in Bellevue, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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