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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 an Associate Machine Learning Engineer, you will specialize in engineering solutions that support research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities.
The Secure AI Lab within the SEI’s AI Division focuses on improving the security and robustness of AI systems. As part of the world\-class research community at Carnegie Mellon University, the Secure AI Lab conducts and applies cutting\-edge research to protect AI systems from adversaries who aim to manipulate the system to learn, do, or reveal something it isn’t supposed to.
The Secure AI Lab consists of machine learning research scientists, machine learning engineers, and software developers who work together to solve problems in the following areas:
- Counter AI Research: Study threat models targeting AI and ML algorithms, understand the behaviors of AI algorithms, identify weak points, and design novel ways to subvert AI and ML systems.
- AI and ML Algorithm Defense Research: Create practical mitigations and defenses for observed attacks affecting AI and ML algorithms and evaluate the effectiveness of defensive techniques.
- Applied Adversarial Machine Learning: Advance the state of the art in adversarial machine learning by developing and transitioning capabilities to government sponsors.
As an engineer, you will solve problems for government sponsors by analyzing, designing, and building responsible AI systems.
Your day\-to\-day engineering tasks will include:
- Identifying and investigating emerging AI and AI\-adjacent technologies.
- Defining and refining processes, practices, and tools for working with AI.
- Designing and building well\-engineered prototypes of AI systems.
- Transitioning and providing guidance on AI capabilities to government sponsors.
Duties
- Building Machine Learning Models and Systems: You will work with machine learning frameworks such as TensorFlow, PyTorch, Torch, and Caffe and modern programming languages including Python, C/C\+\+, and Java. You will build and work with data pipelines, ETL processes, and backend systems. You will work with, extend, and implement state\-of\-the\-art machine learning methods.
- Technical Experimentation: You will experiment with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision, natural language processing, planning and scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.
- Testing and evaluation. You'll conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments. You'll evaluate systems for performance and security. You'll test capabilities using novel testing and analysis techniques.
- Collaboration. You'll actively participate on teams of developers, researchers, designers, and technical leads. You'll collaborate with researchers and our government customers to understand challenges, needs, and possible solutions.
- Mentoring. You'll contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.
Knowledge and Experience
- Comprehensive knowledge of machine learning; previous experience in adversarial machine learning desirable but not required
- A track record of using well\-established engineering practices to solve difficult problems
- An understanding of how to convert research results into functioning prototypes or capabilities
- Experience leading technical projects in novel areas with limited previous work to build upon
- Strong written and verbal communication skills; able to convey complex technical ideas in a layperson’s terms
- Ample experience with publishing written or technical artifacts showcasing your work
- Strong collaboration skills for working with colleagues and sponsors
- Willingness to guide and mentor junior team members
Requirements
- A bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or related discipline with three (3\) years of experience; OR MS in the same fields with one (1\) year of experience; OR PhD in a relevant discipline.
- Willingness to work onsite 5 days per week at SEI offices in Pittsburgh, PA or Arlington, VA.
- You will be subject to a background investigation and must be able to obtain and maintain an active Department of War security clearance.
- Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.
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
More Information:
- Please visit “ Why Carnegie Mellon ” to learn more about becoming part of an institution inspiring innovations that change the world.
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- Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran .
- Statement of Assurance
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 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.
Carnegie Mellon University AI Hiring
Carnegie Mellon University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
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 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 3,823 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).
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 3,823 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.
Frequently Asked Questions
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