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Causal Inference Phd Internship Jobs (NOW HIRING)

airbnb, Inc.

$140 - $210/hr

... with a Phd degree. * Strong knowledge of causal inference, experimentation, applied statistical modeling, and end-to-end ML development. * Skilled in statistical programming (Python or R) and ...

New

Apple Inc.

Applied Scientist

Culver City, CA · On-site

$150 - $210/hr

Engineer end‑to‑end scalable and robust Causal Inference products which provide Apple with an ... PhD in related field. * Hands‑on experience leveraging Generative AI to improve productivity and ...

Apple

Applied Scientist

Austin, TX

$175K - $308K/yr

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... PhD in related field Hands-on experience leveraging Generative AI to improve productivity and ...

Apple

Applied Scientist

Austin, TX

$175K - $308K/yr

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... PhD in related field Hands-on experience leveraging Generative AI to improve productivity and ...

Docugami

Data Science PhD Intern

Kirkland, WA · On-site

We're now looking for AI, NLP, Machine Learning, Data Science, and Math PhD Interns to work ... causal inference, and other related disciplines * Programming skills and familiarity of modern ML ...

Docugami

Data Science PhD Intern

Kirkland, WA

We're now looking for AI, NLP, Machine Learning, Data Science, and Math PhD Interns to work ... causal inference, and other related disciplines * Programming skills and familiarity of modern ML ...

Roku, Inc.

Sr. Research Data Scientist

Boston, MA · On-site

$330 - $375/hr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

New

Roku

Senior Research Data Scientist

Boston, MA · On-site

$330K - $375K/yr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

Roku

Senior Research Data Scientist

San Jose, CA

$330K - $375K/yr

PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference * 10+ years of experience applying causal inference and machine learning ...

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How much do causal inference phd internship jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for causal inference phd internship in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is a causal inference PhD internship?

A Causal Inference PhD Internship is a specialized research position for doctoral students focused on causal inference, which involves determining cause-and-effect relationships from data. Interns typically work with large datasets, advanced statistical models, and machine learning techniques to answer questions about how variables influence one another. These internships are often offered by tech companies, research labs, or policy organizations and provide hands-on experience in designing experiments, analyzing observational data, and developing new methodologies. The goal is to bridge academic research with real-world applications, contributing to projects that require rigorous causal analysis.

What types of projects does a causal inference PhD intern typically work on during their internship?

Causal Inference PhD interns often engage in projects that involve designing and analyzing experiments or observational studies to draw valid conclusions about cause-and-effect relationships. These projects might include developing statistical models, collaborating with data scientists and product teams, and presenting findings to inform business or policy decisions. Interns usually have the opportunity to work with large-scale, real-world data, and are encouraged to publish or present their work at conferences, supporting both professional growth and academic development.

What are the key skills and qualifications needed to thrive as a causal inference PhD intern, and why are they important?

To thrive as a Causal Inference PhD Intern, you need a strong background in statistics, econometrics, and causal inference methods, often supported by advanced graduate studies in a related field. Familiarity with statistical programming languages such as R or Python, and experience using data analysis tools and frameworks like Stata or TensorFlow Probability, are typically required. Excellent problem-solving abilities, critical thinking, and the ability to communicate complex concepts clearly help you stand out in this role. These skills and qualities are crucial for designing robust experiments, drawing reliable conclusions, and effectively collaborating with interdisciplinary research teams.

What is the difference between Causal Inference Phd Internship vs Data Scientist Internship?

AspectCausal Inference Phd InternshipData Scientist Internship
Required CredentialsPhD in statistics, economics, or related fieldBachelor's or Master's in CS, statistics, or related field
Work EnvironmentResearch-focused, academic or industry research teamsData analysis, modeling, and business insights
Employer & Industry UsageResearch institutions, tech companies, financeTech firms, startups, finance, healthcare
Search & Comparison IntentFocus on causal inference research rolesBroader data analysis roles

While a Causal Inference Phd Internship emphasizes research in causal analysis with advanced credentials, a Data Scientist Internship covers broader data analysis skills suitable for various industries. Both roles involve working with data, but their focus, required background, and career paths differ significantly.

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Causal Inference Phd Internship jobs near you
Infographic showing various Causal Inference Phd Internship job openings in the United States as of August 2026, with employment types broken down into 10% Internship, 56% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Applied Scientist Intern - Monetization Technology - Global Frontier Tech Recruitment Program -[...]

SpeedyApply LLC

San Jose, CA • On-site

$17.50 - $23.50/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands‑on learning, enriching community‑building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Team Introduction: Global Monetization Product and Technology team are building the next‑generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

Topic Content: This topic dives deep into TikTok's core global advertising scenarios, driving innovation and implementation of the cutting‑edge generative technologies in search, recommendation, and advertising. By deeply integrating foundation models with the advertising business, we address key technical challenges in Large Recommender Models and Large Language Models (LLMs) to build a next‑generation intelligent advertising engine with autonomous decision‑making capabilities.

Our research covers cutting‑edge directions, including Large Recommender Model scaling laws, end‑to‑end unified modeling, generative full‑link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra‑long sequence modeling, and causal inference. We tackle extreme challenges of trillion‑level features and millisecond responses, advancing advertising recommendation toward the foundation model paradigm to achieve dual improvements in monetization efficiency and user experience.

Responsibilities:

  1. Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
  2. Build an intelligent ad placement system optimized for users' Long‑Term Value (LTV) and long‑term ROAS, achieving an optimal balance between commercial value and user experience.
  3. Optimize the full‑process training and online inference framework for foundation models, balance computing power costs and real‑time response performance, and resolve the performance‑latency trade‑off in real‑world deployment.

Minimum Qualifications:

  1. Currently pursuing a PhD in Computer Science, Computer Engineering, or a related technical discipline.
  2. Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV.
  3. Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and practical experience in areas such as single‑modal LLM application and deployment.

Preferred Qualifications:

  1. Priority will be given to candidates with research results and extensive practical relevant fields, such as outstanding performance in natural language processing, computer vision, data modeling, or algorithm optimization, etc.
  2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required.
  3. Strong publications record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, NACCL, CVPR, ICCV, and ECCV) is a plus.
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