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About This Role
Reston, Virginia, United States Category Engineering, Product, \& UX Design
JOB DESCRIPTION
At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high\-performing teams, and design products people love. But we are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close\-knit, high\-functioning teams with an unwavering dedication to serving our customers.
When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.
Senior Applied Scientist
Why We Have This Role
We are looking for a talented and innovative Senior Applied Scientist to bring our Machine Learning and Artificial Intelligence R\&D to the next level. Our goal is to personalize the Qualtrics experience using ML/AI features showcasing Qualtrics data as a core value proposition and competitive advantage. Applied scientists partner with application teams to deliver algorithms and models that drive efficiency, innovation, and growth. They identify business needs and define strategies where Qualtrics can most benefit from AI technologies and they conduct modeling with deep expertise and rigor, including model selection, training, fine\-tuning, customization, explainability, and evaluation.
How You’ll Find Success
- Leverage your deep knowledge of AI principles, including machine learning, natural language processing, and reinforcement learning.
- Use your understanding of both supervised and unsupervised learning techniques, and their applications in building intelligent systems.
- Develop and optimize algorithms for building scalable and efficient AI applications.
- Tackle challenging problems in creative ways, leveraging generative models to address real\-world use cases and drive innovation.
- Use effective communication skills to articulate technical concepts to non\-technical stakeholders and gather requirements for AI application development.
- Design and execute evaluation strategies for agentic systems, including rubric\-based success criteria, multi\-turn conversation simulation, and LLM\-as\-a\-judge frameworks.
- Show strong programming skills in languages like Python, along with proficiency in deep learning frameworks such as TensorFlow, PyTorch, or similar.
How You’ll Grow
- Passion for leveraging cutting\-edge AI technology to create innovative applications that have a meaningful impact on businesses, industries, and society.
- Commitment to developing AI applications that adhere to ethical standards and promote positive societal impact while minimizing potential risks.
- Drive to push the boundaries of what's possible with AI, and to contribute to the advancement of the field through research, experimentation, and collaboration.
- Willingness to stay updated with the latest advancements in AI research and technology, and to continuously learn and adapt to new methodologies and best practices.
- Agility to pivot and iterate on AI applications based on feedback, emerging trends, and changing business requirements.
Things You’ll Do
- Work as part of a multidisciplinary team to research, implement, evaluate, optimize, productize and maintain cutting\-edge machine learning models to meet the demands of our rapidly growing business.
- Stay on top of the latest developments in machine learning and related research, and present research findings with the broader community.
- Work closely with, and incorporate feedback from other specialists, engineers, and product managers.
- Lead and engage in design reviews, modeling discussions, requirement definitions and other technical activities in diverse capacity.
- Mentor and grow junior scientists, drive best practices for experimentation, reproducibility, monitoring and lifecycle management, and ensure models are reliable, scalable and impactful in production.
- Champion Evaluation\-Driven Development (EDD) by embedding automated testing, risk\-based assessments, and production monitoring into the full agentic lifecycle.
What We’re Looking For On Your Resume
- Bachelors and Ph.D in Computer Science or related fields.
- 2\+ years of post graduate industrial research experience in machine learning, NLP, information retrieval, deep learning or a related field.
- Deep learning implementation expertise (MxNet, TensorFlow, PyTorch etc)
- Excellent communication, writing and presentation skills.
- Excellent command of at least one modern programming language (preferably Python)
- Excellent problem solving ability.
- Deep understanding of machine learning model life cycle management.
- Depth in one or more of the following: Natural Language processing, information retrieval, speech processing, deep learning, reinforcement learning, etc.
- Knowledge of or experience in building production quality and large scale deployment of applications related to machine learning.
- Comfortable working in a fast paced, highly collaborative, dynamic work environment.
- Experience in machine learning systems (e.g. SageMaker, MLFlow), and deep learning frameworks (e.g. TensorFlow, PyTorch, MXNet etc).
- Preference for a publication record in top\-tier ML and NLP conferences (e.g. NeurIPS, ICML, SIGIR, ICLR, ACL, EMNLP, etc.).
- Proven track record in evaluating complex, multi\-turn agentic systems. Deep experience with observability tools, evaluating tool\-use reliability, and implementing systematic benchmarking in CI/CD pipelines.
What You Should Know About This Team
- The Core AI organization provides AI/ML research and development services for all product lines.
- We are a global team spanning across the US, Canada and Europe.
- We stay on top of the latest AI technologies and apply the state of the art to customer business problems.
- We develop agentic applications, spanning both conversational and non\-conversational interfaces, to empower our diverse global clientele.
- We architect robust builder platforms that enable customers to seamlessly design and deploy their own custom agents.
Our Team’s Favorite Perks and Benefits
- Wellness Reimbursement for $300 per quarter for wellness activities including gym memberships, spa massages, workout equipment, meditation apps, and much more.
- $1800 Experience bonus to be used for an “Experience” of your choosing
- Amazing QGroup Communities; MOSAIQ, Green Team, Qualtrics Pride, Q\&Able, Qualtrics Salute, and Women’s Leadership Development, which exist as places for support, allyship, and advocacy.
The Qualtrics Hybrid Work Model: Our hybrid work model is elegantly simple: we all gather in the office three days a week; Mondays and Thursdays, plus one day selected by your organizational leader. These purposeful in\-person days in thoughtfully designed offices help us do our best work and harness the power of collaboration and innovation. For the rest of the week, work where you want, owning the integration of work and life. \#hybrid
*Qualtrics is an equal opportunity employer meaning that all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other protected characteristic.*
*Applicants in the United States of America have rights under Federal Employment Laws:Family \& Medical Leave Act,Equal Opportunity Employment,**Employee Polygraph Protection Act*
*Qualtrics is committed to the inclusion of all qualified individuals. As part of this commitment, Qualtrics will ensure that persons with disabilities are provided with reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please let your Qualtrics contact/recruiter know.*
*Not finding a role that’s the right fit for now? Qualtrics Insiders is the one\-stop shop for all things Qualtrics Life. Sign up for exclusive access to content created with you in mind and get the scoop on what we have going on at Qualtrics \- upcoming events, behind the scenes stories from the team, interview tips, hot jobs, and more. No spam \- we promise! You'll hear from us two times a month max with fresh, totally tailored info \- so be sure to stay connected as you explore your best role and company fit.*
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 3,708 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Qualtrics, 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 197 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Qualtrics AI Hiring
Qualtrics has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist. Positions span Seattle, WA, US, Reston, VA, US. Compensation range: $262K - $262K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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