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About This Role
Overview
The Copilot Search Multimedia Index Selection team is looking for a Data and Applied Scientist II to help build the next generation platform for Bing and Microsoft AI.Our mission is to construct the most comprehensive, rich, clean, diverse, and fresh image index that serves as the foundation for image\-related experiences across Microsoft. We develop advanced AI and machine learning solutions for image content discovery, selection, ranking, and processing to improve the quality and relevance of our multimedia products.Maintaining a comprehensive, diverse, and up\-to\-date image index presents unique technical challenges. We continuously innovate to improve quality, scalability, and efficiency while supporting billions of images and balancing performance, freshness, cost, and latency requirements.Some of the key challenges you will work on include:Image Content Discovery: Developing approaches to efficiently discover and understand image content across the web.Machine Learning for Selection and Ranking: Building, training, and evaluating machine learning, deep learning, and multimodal models for content understanding, selection, and ranking using both text and image signals.Freshness and Real\-Time Systems: Contributing to data pipelines and models that support near real\-time indexing and content freshness.AI\-Powered Platform Innovation: Exploring and implementing new AI capabilities that improve platform intelligence and operational efficiency.This is an exciting opportunity for someone who enjoys solving challenging technical problems using data, machine learning, and AI. In this role, you will collaborate closely with scientists, engineers, and product teams to develop and deploy production\-ready solutions. You will conduct experiments, analyze large\-scale data sets, build and evaluate machine learning models, and translate insights into impactful improvements for Microsoft AI products.As a Data and Applied Scientist II, you will apply statistical, machine learning, and deep learning techniques to analyze and interpret multimodal content, develop features and models that improve system capabilities, and help scale AI solutions to large\-volume production environments. You will contribute to advancing state\-of\-the\-art approaches while delivering measurable impact to products used by millions of customers.
Responsibilities
- Develop expertise in relevant machine learning, deep learning, and multimodal AI techniques, and apply them to solve challenging problems in image indexing, content understanding, selection, and ranking.
- Partner with scientists, engineers, and product managers to understand business and product requirements, translate them into data\-driven solutions, and deliver measurable product impact.
- Design, implement, and evaluate machine learning and deep learning models, including Large Language Models (LLMs), Small Language Models (SLMs), and multimodal models for document understanding, content discovery, parsing, clustering, selection, and ranking.
- Conduct large\-scale data analysis and experimentation to identify opportunities for improving image index quality, freshness, relevance, and diversity.
- Develop robust evaluation methodologies and analyze experiment results to drive model improvements and support data\-informed decision making.
- Contribute to near real\-time indexing systems and AI\-powered platform capabilities that improve multimedia content discovery and user experiences.
- Work with petabyte\-scale datasets and Big Data technologies to build, validate, and optimize machine learning solutions for production environments.
- Document experiments, model performance, methodologies, and key learnings, and communicate findings effectively with teammates and stakeholders.
- Follow responsible AI, privacy, security, and data governance best practices throughout the model development lifecycle.
- Collaborate across Microsoft with engineers, scientists, and product teams to develop, deploy, and operate scalable AI solutions that serve billions of multimedia documents.
- Stay current with advancements in machine learning, deep learning, generative AI, and multimodal systems, and apply relevant innovations to improve product capabilities.
- Contribute to the delivery of state\-of\-the\-art models and data\-driven features that enhance the Microsoft AI Multimedia Index platform.
Qualifications Required Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2\+ years related experience (e.g., statistics, predictive analytics, research)
+ OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1\+ year(s) related experience (e.g., statistics, predictive analytics, research)
+ OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
+ OR equivalent experience.
Preferred Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5\+ years related experience (e.g., statistics, predictive analytics, research)
+ OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3\+ years related experience (e.g., statistics, predictive analytics, research)
+ OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1\+ year(s) related experience (e.g., statistics, predictive analytics, research)
+ OR equivalent experience.
- Solid machine learning / computer vision / natural language processing experience and results in academy or industry. Publications in major ML/IR/NLP/CV conferences. Examples: ICML, NIPS, SIGIR, ACL, EMNLP, CVPR, KDD.
- Experience in open source (spark, hbase, hadoop etc) or microsoft internal tech (cosmos, autopilot, azure) are big plus as well as most of our project/scenario is across system and AI areas.
- Good communication skills and ability to work in collaborative environment.
- Good design and problem\-solving skills and an ability to innovate and solve challenging technical problems.
- Passion and self\-motivation.
- Embrace engineering excellence and delivering quality results at scale.
\#MicrosoftAI
Applied Sciences IC3 \- The typical base pay range for this role across the U.S. is USD $102,100 \- $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 \- $219,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us\-corporate\-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.
Salary Context
This $102K-$219K range is in the lower quartile for Research Scientist roles in our dataset (median: $195K across 149 roles with salary data).
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 4,317 AI roles we're tracking, Research Scientist positions make up 4% of the market. At Microsoft, 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 $222,200 based on 378 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($160K) sits 28% below the category median. Disclosed range: $102K to $219K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
Frequently Asked Questions
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