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About This Role
We're looking for Ph.D. students specializing in Applied Science to intern during Fall 2026 (12 weeks). As a Ph.D. intern, you will be embedded in a product team working on solving real\-world Uber problems and will have the opportunity to partner closely with other Applied and Data Scientists, Software Engineers, Product Managers, and other cross functional partners.
About the Team
The Commerce, Trust, Safety and Support (CTSS) team applies data science and analytics to drive initiatives across all core service elements, including Customer Support, Safety, Risk, Insurance, and Identity. As a member of the team you will conduct deep\-dive analyses, design and analyze experiments, and support the development of machine learning models to make using our platform as smooth and magical as possible for all users. You will play an influential role in driving critical product and policy decisions. A critical aspect of this role is being very hands\-on, not only in model development and prototyping, but also through deployment and launch, helping to structure projects from the initial idea through to final implementation.
What You'll Do* Work with a mentor closely to define a business problem, scope a project, develop, and prototype the solution using data\-driven approaches
- Perform deep\-dive analyses to discover root causes for safety issues and changes in trends
- Present findings to leaders to inform decisions
- Support statistical and machine learning efforts including modeling, experimentation, signal processing, time series analysis, geospatial analysis, natural language processing, large language model interactions and more
- Get an opportunity to drive the implementation and scaling of developed solutions.
- Get an opportunity to utilize and learn software engineering tools/concepts, including ML web applications (Streamlit, Flask, etc.), real\-time databases, big data tools (Spark, Ray), and LLM tools and frameworks (HuggingFace Transformers, LangChain) and Generative AI APIs (OpenAI, Google).
Basic Qualifications* Current Ph.D. student majoring in Operations Research, Mathematics, Computer Science, Statistics, Machine Learning, or other related quantitative fields
- Candidates should have at least one semester/quarter left of their education after finishing the internship
Preferred Qualifications* Strong foundation in statistics, machine learning, optimization, economics, analytics, experimental design, and causal inference, with experience applying these methods to solve complex business problems
- Experience with exploratory data analysis, statistical modeling, experimentation, and model development
- Proficiency in SQL and familiarity with programming languages such as Python and/or R
- Demonstrated problem\-solving skills, analytical rigor, and a research\-oriented mindset, with the ability to drive projects from ideation and experimentation through prototyping, implementation, and deployment
- Interest in operating as a full\-stack Applied Scientist, taking ownership of the end\-to\-end solution lifecycle and collaborating closely with cross\-functional technical and business stakeholders
- Excellent communication skills, independence, and strong execution, with a bias toward action and a track record of delivering results
Interest in software engineering fundamentals and productionizing analytical solutions, including familiarity with tools and concepts such as ML web applications (e.g., Streamlit, Flask), real\-time databases, distributed computing frameworks (e.g., Spark, Ray), and modern AI/LLM frameworks (e.g., Hugging Face Transformers, LangChain)
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For San Francisco, CA\-based roles: The base hourly rate amount for this role is USD$67\.00 per hour. For Sunnyvale, CA\-based roles: The base hourly rate amount for this role is USD$67\.00 per hour. US interns are eligible for comprehensive health coverage, life and disability insurance, and additional benefits. They are also eligible for paid time off.
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,823 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Uber, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 280 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Uber AI Hiring
Uber has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Sunnyvale, CA, US, New York, NY, US. Compensation range: $180K - $224K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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