Interested in this AI/ML Engineer role at University of New Mexico?
Apply Now →Skills & Technologies
About This Role
AI Research Post Doctoral Fellow
------------------------------------
Posting Number req37416
Employment Type Faculty
Faculty Type Research
Hiring Department Ctr Adv Research Computing Gen Adm (663B)
Academic Location Vice President for Research
Campus Main \- Albuquerque, NM
Benefits Eligible Postdoctoral Fellows may be eligible to receive certain UNM benefits . See the Benefits home page for more information.
Position Summary
The University of New Mexico’s Center for Advanced Research Computing (CARC), within the Department of Computer Science, seeks a full\-time Postdoctoral Researcher to lead development of an open\-source agentic artificial intelligence platform as part of a federally funded, multi\-institution research initiative. The Postdoctoral Researcher will design and build the project’s agentic AI stack—open\-weight large language models served at scale, retrieval\-augmented generation (RAG) pipelines, a Model Context Protocol (MCP) server framework, sandboxed execution, and multi\-agent orchestration—and will direct a distributed engineering effort spanning the collaborating institutions. The position is supervised by and co\-located with the Principal Investigator at CARC, with secondary mentorship from collaborating co\-investigators at partner institutions. All work follows open\-source, reproducible\-research practice.
Primary Duties and Responsibilities
- Leads the design, development, and evaluation of the project’s agentic AI platform, including the serving of open\-weight large language models (e.g., vLLM\-served models), retrieval\-augmented generation pipelines, the Model Context Protocol (MCP) server framework, sandboxed code execution, and multi\-agent orchestration.
- Directs and coordinates a distributed engineering effort, leading regular technical meetings with the partner\-institution team and graduate research assistants, and presenting at design reviews and project milestones.
- Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high\-performance GPU systems (e.g., H100 / A100 / L40S) and national cloud allocations, and documents empirical hardware and performance findings.
- Leads and contributes to peer\-reviewed, open\-access publications (target of at least two first\-author papers), and disseminates results through public code repositories, containerized reproducible workflows with persistent identifiers (DOIs), and FAIR data practices.
- Participates in security and responsible\-AI review activities, including prototype security review and engagement with the project’s external AI ethics advisory board.
- Co\-teaches research\-computing and data\-science training workshops (e.g., R, Python, Linux, ML/AI pipelines) and contributes training modules to the project’s education and workforce\-development activities.
- Co\-mentors graduate research assistants contributing to the agentic AI and MCP workstreams.
- Participates in the annual program meeting and represents the project’s technical progress to collaborators, sponsor program staff, and the broader research community.
- Contributes to grant reporting and to the preparation of follow\-on proposals, including empirical hardware\-specification and benchmarking content.
- Performs related duties as assigned in support of the project’s goals and the Fellow’s professional development.
Mentoring and Professional Development
Consistent with UNM’s expectations for postdoctoral training, the Fellow and mentor will jointly prepare an Individual Development Plan (IDP) within 30 days of hire, organized around the National Postdoctoral Association core competencies, with semiannual review. The Fellow will receive weekly one\-on\-one mentorship from the PI, structured career advising across academic, national\-laboratory, and industry pathways, grant\-writing experience, and visibility through the project’s national partner network. The Fellow will complete UNM’s Responsible Conduct of Research (RCR) training within the first six months.
Due to budgetary constraints, we are unable to sponsor or take over sponsorship of an employment Visa. Applicants must be authorized to work in the United States on a full\-time basis.
QualificationsMinimum Qualifications:
- Ph.D. (or terminal degree) in Computer Science, Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Computer Engineering, Computational Science, Data Science, Management Information Systems, Information Science, or a closely related field, completed by the date of appointment.
- Demonstrated research experience in machine learning, applied artificial intelligence, distributed systems, or research software engineering, as evidenced by publications, software, or other scholarly products.
- Programming proficiency in one or more relevant languages (e.g., Python, Rust, Go, C/C\+\+, JavaScript/TypeScript, or R)
Preferred Qualifications:
- Experience with large language models, including model serving (e.g., vLLM), retrieval\-augmented generation, agentic/multi\-agent frameworks, or the Model Context Protocol (MCP).
- Experience developing and deploying containerized, reproducible workflows (e.g., Docker, Kubernetes/Helm) on HPC or cloud infrastructure (e.g., SLURM, OpenStack, ACCESS\-CI resources).
- Experience building APIs and services (e.g., FastAPI, OpenAI\-compatible inference endpoints) and integrating authentication and orchestration tooling.
Track record of open\-source software development, code review, and FAIR/open\-science practice (public repositories, DOIs, reproducible pipelines).
- Experience leading or coordinating distributed teams, mentoring students, or teaching technical workshops.
- A demonstrated commitment to cultivate an understanding of the rich and varied cultures of New Mexico and to the success of the university's mission to serve local and global communities
Application Instructions
Only applications submitted through the official UNMJobs site will be accepted. If you are viewing this job advertisement on a 3rd party site, please visit UNMJobs to submit an application.
Please submit a CV detailing relevant experience and research as well as a cover letter discussing your unique qualifications for the position.
Applicants who are appointed to a UNM faculty position are required to provide an official certification of successful completion of all degree requirements prior to their initial employment with UNM.
For Best Consideration For best consideration, please apply by . This position will remain open until filled.
The University of New Mexico is committed to hiring and retaining a diverse workforce. We are an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other protected class.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of New Mexico, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
University of New Mexico AI Hiring
University of New Mexico has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, 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 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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.