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About This Role
The SEI AI Division
At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering challenges related to the practical design and implementation of AI technologies and systems. We currently lead a community\-wide movement to mature the discipline of AI Engineering for Defense and National Security.
As our government customers adopt AI and machine learning to provide leap\-ahead mission capabilities, we
- Build real\-world, mission\-scale AI capabilities through solving practical engineering problems
- Discover and define the processes, practices, and tools to support operationalizing AI for human\-centered, robust, secure, and scalable mission capabilities
- Prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
- Identifyand investigate emerging AI and AI\-adjacent technologies that are rapidly transforming the technology landscape
Are you creative, curious, and collaborative? Do you enjoy doing meaningful and complex work? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.
Position Summary: A s a senior research scientist with proven expertise in advanced computing , you will identify , shape, apply, conduct, and lead research in support of critical U.S. government needs . The ideal candidate will have a strong background with hands\-on experience in one or more of the following technology areas: hardware/software co \-d esign , performance optimization with heterogeneous and alternative computing systems ( CPU/GPU/NPU/etc. ) , FPGA design, high\-performance computing (HPC) , distribute d and scalable computing, compiler technology , and algorithm optimization . A dditional expertise in applying AI/ML to electromagnetic spectrum operations is desired .
Duties
- Lead novel research in advanced computing and applied machine learning.
- Lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders.
- C onduct rapid prototyping to demonstrate and evaluate technologies in relevant environments.
- Test and e valuate new and novel systems for performance and usability .
- C ollaborate with researchers , developers, engineers , designers, technical leads , campus labs, and our government customers to understand challenges, needs, and potential solutions.
- Mentor and teach junior staff , lead design sessions, and establish technical norms for the division .
- E xplore the latest advanced hardware, including FPGAs, CGRAs, TPUs, and emergent technologies such as neuromorphic and analog processors.
- W ork with division leadership and colleagues to plan, develop, expand, and carry out an overall research strategy, and influence the national research agenda regarding future technology.
- Develop and deliver compelling technical presentations for diverse audiences, tailor content for varied stakeholders, speak at conferences and workshops , and publish in pertinent peer\-reviewed journals .
Requirements
- BS in Electrical Engineering, Computer Science, or related discipline with ten (10\) years of experience; OR MS in the same fields with eight (8\) years of experience; OR PhD with five (5\) years of experience .
- Able to travel to the Arlington SEI office, sponsor sites, conferences, and offsite meetings on occasion. Moderate (25%) travel outside of your home location.
- You will be subject to a background check investigation and will need to obtain and maintain a Department of War security clearance.
Knowledge, Skills, and Abilities
- A published track record of conducting research and applying scientific methods.
- Mastery of C/C\+\+, Python, Verilog/VHDL, and/or other pertinent languages and software tools and frameworks.
- Experience leading research projects and collaborating across research teams and mentoring other researchers.
- Experience writing and delivering successful research proposals to funding agencies and leading the resulting projects.
- Experience working with Department of War and Intelligence Community.
- Familiarity with emerging trends and opportunities in industry and government.
Joining the CMU team opens the door to an array of exceptional benefits.
Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits , take well\-deserved breaks with ample paid time off and observed holidays , and rest easy with life and accidental death and disability insurance.
Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access , and much more!
For a comprehensive overview of the benefits available, explore our Benefits page .
At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.
Are you interested in an exciting opportunity with an exceptional organization?! Apply today!
Location
Pittsburgh, PA
Job Function
Software/Applications Development/Engineering
Position Type
Staff – Regular
Full Time/Part time
Full time
Pay Basis
Salary
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 Carnegie Mellon University, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Carnegie Mellon University AI Hiring
Carnegie Mellon University has 2 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in Pittsburgh, PA, US.
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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