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Causal Inference Machine Learning Postdoctoral Jobs in Berkeley, CA

Faire

Staff Applied AI/ML Scientist

San Francisco, CA · On-site

$246K - $339K/yr

About this role Faire leverages the power of machine learning and data insights to revolutionize ... Experience with relevant technical methods (causal inference, predictive/LTV modeling ...

OpenAI

Economist

San Francisco, CA · On-site

Familiarity with causal inference, machine learning methods, or structural modeling. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Berkeley, CA

Causal Inference Machine Learning Postdoctoral information

See Berkeley, CA salary details

$43.5K

$66.4K

$74.7K

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 Berkeley, CA is $66,393.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,500.00 and $69,200.00 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.

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

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

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What cities near Berkeley, CA are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Berkeley, CA 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 Berkeley, CA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 20% Part Time, and 3% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $66,393 per year, or $31.9 per hour.

Machine Learning Researcher

San Francisco, CA • On-site

Fluency Digital, Inc.
See more jobs at Fluency Digital, Inc.external

Other

Posted 27 days ago

Job description

Early-stage AI lab building intelligence that learns causality through physics.

You are a researcher who believes true intelligence emerges from understanding physical laws rather than statistical patterns in language. Your days will be spent architecting novel PyTorch models that infer causal structures from raw data, moving beyond correlation to grasp the mechanics of how the world works. We offer the autonomy to pursue deep, fundamental questions in a focused, on-site environment in San Francisco where every line of code advances the frontier of causal reasoning.

What we're looking for:
  • 1+ years of hands-on experience building and training models in PyTorch
  • Strong foundation in Python and scientific computing libraries
  • Deep interest in causal inference, physics-based modeling, or representation learning
  • Proven ability to translate complex research concepts into robust engineering implementations
  • Preference for deep focus and collaborative problem-solving over corporate process
  • Must be based in the United States.
Tech stack:

Python, PyTorch

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