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About This Role
At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions 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 robust, secure, scalable, and human\-centered mission capabilities
- prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
- identify and investigate emerging AI and AI\-adjacent technologies that are rapidly transforming the technology landscape
Are you creative, curious, energetic, collaborative, technology\-focused, and hard\-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.
Overview
As a Machine Learning Engineer who can take research ideas from concept to prototype , you will lead independent applied‑research projects for defense‑focused missions.
The ideal candidate is comfortable across the full stack (data pipelines, model development, API services, and secure deployment) and eager to explore novel AI and ML theory while delivering mission‑scale capabilities.
The Mission Innovation Lab within the SEI’s AI Division works with the defense and national security community to translate the “ recently possible” in AI into reliable mission and warfighting capabilities.
Key Responsibilities
- Design, implement, and evaluate state‑of‑the‑art ML models (computer‑vision, NLP, planning, etc.) using frameworks such as TensorFlow, PyTorch , Torch, or Caffe .
- Build and maintain robust data pipelines, ETL processes, and backend services in Python, C/C\+\+, and Java .
- Lead rapid‑prototyping efforts, translate research results into operational prototypes, and test for performance, robustness, and security .
- Define and refine DevSecOps practices for ML (model registries, containerized deployment, continuous integration/continuous delivery, security scanning) .
- Mentor junior team members, collaborate with researchers, government customers, and other engineers, and contribute to technical strategy for the lab.
Required Qualifications
- B.S. in Computer Science, Electrical Engineering, Statistics, or related field with 10 years of experience ; OR M.S. with 8 years ; OR Ph.D. with 5 years of relevant experience.
- Ability to obtain and maintain an active Department of War security clearance.
- You must be able and willing to work onsite 5 days per week at an SEI office in either Pittsburgh, PA or Arlington, VA.
- Strong experience in one or more programming language such as Python, C/C\+\+, and Java; comfortable developing production‑grade code and APIs.
- Solid understanding of ML theory, statistical learning, and common algorithms.
- Hands‑on experience with TensorFlow, PyTorch , Torch, Caffe, or similar deep‑learning libraries.
- Familiarity with CI/CD pipelines, container orchestration (Docker/Kubernetes), model versioning, and security‑focused tooling.
Desired Experience
- Proven track record of independent applied‑research projects that resulted in demonstrable prototypes or operational capabilities.
- Publications or open‑source contributions in AI and ML , especially in adversarial or robust ML.
- Experience working on defense or other high‑impact government programs .
- Ability to quickly learn emerging AI and ML technologies and translate them into mission‑relevant solutions.
Knowledge, Skills, \& Abilities
- Deep technical knowledge of modern ML methods and ability to extend them to novel domains.
- Excellent written and verbal communication skills ; capable of presenting complex ideas to technical and non‑technical audiences.
- Strong collaborative mindset; experience working in interdisciplinary teams and mentoring peers.
- High degree of scientific curiosity and a proactive, self‑directed work style.
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
Arlington, VA, 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
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Carnegie Mellon University, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 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.
Carnegie Mellon University AI Hiring
Carnegie Mellon University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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