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Causal Inference Machine Learning Postdoctoral Jobs in California

Spotter

Machine Learning Scientist

Culver City, CA · Remote

Applying offline policy evaluation, counterfactual evaluation, causal inference, or related ... Experience training, evaluating, tuning, and deploying machine learning models across deep learning ...

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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?

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 California? For Causal Inference Machine Learning Postdoctoral jobs in California, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in California look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in California are:
What cities in California are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in California 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 California as of July 2026, with employment types broken down into 3% Locum Tenens, 83% Full Time, 12% Part Time, 1% Temporary, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Machine Learning Engineer, Causal Inference, Level 5

Snapchat

Palo Alto, CA • On-site

Full-time

Medical

Re-posted 4 days ago

Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.


The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

We're looking for a Machine Learning Engineer to join Snap Inc!

What you'll do:

  • Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business

  • Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data

  • Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies

  • Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability

  • Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure

  • Contribute to rapid iteration cycles while ensuring methodological rigor

Knowledge, Skills & Abilities:

  • Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)

  • Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure

  • Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalM etc.)

  • Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism

  • Comfortable working independently and collaborating across cross-functional teams

  • Strong communication and mentorship skills; able to translate technical insights for non-technical partners

Minimum Qualifications:

  • Bachelor's degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience

  • 5+ years of post-Bachelor's experience in machine learning, with hands-on experience in causal inference or experimentation; or Master's degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience

  • Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques

  • Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems

Preferred Qualifications:

  • Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research

  • Experience with causal inference libraries such as CausalML, EconML or DoWhy

  • Background in deploying models in production settings and working with ML or experimentation infrastructure

  • Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty

  • Experience applying causal inference in domains like personalization, ad or marketplace dynamics

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $209,000-$313,000 annually.


Zone B:

The base salary range for this position is $199,000-$297,000 annually.

Zone C:

The base salary range for this position is $178,000-$266,000 annually.This position is eligible for equity in the form of RSUs.

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