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About This Role
Categories: Collaboration & teamwork, Communication, Critical thinking & problem solving, Leadership, Learning & application, Professionalism, Student, Technology
Department: EERC
EERC Research Assistant (Data Science and Artificial Intelligence) - Graduate Level
- 499555
- Grand Forks, North Dakota, United States
- Institutional Student
- Closing on: Oct 30 2026
Salary/Position Classification
- $24.00 hourly, Non-Exempt (Eligible for overtime)
- Up to 20 hours per week
- 100% Remote Work Availability: No
- Hybrid Work Availability (requires some time on campus): No
Purpose of Position
This is a part-time, non-benefited position. Only UND students are eligible to be hired in this position.
Duties & Responsibilities
We are looking for a graduate student to support applied data science and artificial intelligence (AI) research at the Energy & Environmental Research Center (EERC). The selected student will work with EERC scientists, engineers, and data scientists as part of the Bakken 2.0 Cracking the Code (CtC) initiative, which seeks to advance the understanding and application of enhanced oil recovery (EOR) in the Bakken petroleum system.
The work will focus on using multiple computational and analytical approaches, from data curation, statistics, and exploratory data analysis through machine learning and advanced AI, to extract information and insight from large, complex, multimodal Bakken datasets. These datasets may include well construction and completion information, production histories, reservoir and geological properties, geospatial data, engineering data, simulation results, technical literature, reports, figures, and other structured and unstructured information.
A particular emphasis will be placed on investigating how emerging AI capabilities, including large language models, multimodal AI, AI-assisted scientific workflows, and agentic AI, can complement conventional data science methods to accelerate scientific discovery and decision support.
The selected individual will work closely with EERC research scientists, engineers, and data scientists to develop practical, reproducible analytical workflows and translate complex datasets into information that supports research and engineering decisions.
Duties & Responsibilities:
- Assist in acquiring, organizing, curating, integrating, and quality-controlling structured and unstructured datasets relevant to Bakken research and enhanced oil recovery (EOR).
- Develop reproducible Python-based workflows for data processing, exploratory data analysis, visualization, statistical analysis, and machine learning.
- Apply statistical and machine-learning techniques to identify patterns, relationships, clusters, anomalies, and predictive features within complex scientific and engineering datasets.
- Integrate and analyze multimodal information, potentially including tabular, time-series, geospatial, technical-document, image, and other scientific data.
- Evaluate and develop applications of large language models, multimodal AI, and agentic AI for scientific data extraction, synthesis, analysis, reasoning, and knowledge discovery.
- Work with EERC scientists and engineers to evaluate AI- and data-driven results against conventional analytical methods, scientific understanding, and engineering principles.
- Document analytical methods, code, assumptions, data provenance, and results, and contribute to technical reports, presentations, visualizations, and other research products.
Required Competencies
- Strong analytical and quantitative problem-solving skills.
- Ability to work with large, heterogeneous, and potentially incomplete scientific and engineering datasets.
- Ability to work independently while also contributing effectively to multidisciplinary research teams.
- Demonstrated curiosity and willingness to learn unfamiliar scientific and engineering subject matter.
- Ability to evaluate new computational and AI methods critically rather than treating AI-generated results as inherently reliable.
- Strong written and oral communication skills, including the ability to explain technical methods and results to researchers from different disciplines.
- Ability to develop organized, documented, and reproducible computational workflows.
Minimum Requirements
- Enrolled in an M.S. or Ph.D. program in data science, computer science, electrical or computer engineering, applied mathematics, statistics, engineering, geoscience, or a related quantitative field.
- Experience programming in Python for scientific computing, data analysis, or machine learning.
- Experience working with common scientific data-analysis libraries and tools.
- Foundational knowledge of statistics, data analysis, and machine learning.
- Experience organizing, processing, and analyzing complex datasets.
- Excellent communication and interpersonal skills.
- Successful completion of a Criminal History Background Check
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the US and to complete the required employment eligibility verification form upon hire. This position does not support visa sponsorship for continued employment.
Preferred Qualifications
- Python scientific computing and data analysis, including tools such as NumPy, pandas, SciPy, scikit-learn, and visualization libraries.
- Machine learning, deep learning, scientific machine learning, and model evaluation.
- Large language models, generative AI, multimodal AI, and related emerging AI technologies.
- AI agents, agentic workflows, or AI-assisted scientific research and knowledge discovery.
- Analysis and integration of complex scientific data, including geospatial, time-series, engineering, and unstructured data.
- SQL, databases, data engineering, or development of curated and reproducible research datasets.
- High-performance, parallel, distributed, or cloud computing.
- Energy, petroleum engineering, enhanced oil recovery, subsurface science, or related energy applications. Prior petroleum or geoscience experience is beneficial but not required.
To Apply
For full consideration, applications must be received by the closing date and include the following materials:
- Cover Letter
- Resume
Please include in the application if you are currently or have in the past 12 months been employed with the University of North Dakota, the North Dakota University System or any other North Dakota State agency. If so, include which agency/department, as well as how many hours you work a week.
Career Services is here to help students looking for student employment positions at UND by offering individual sessions that include resume, cover letter reviews, and interview preparation. Please schedule an appointment through Hawk Central or email us at [email protected]. For full consideration, all application materials must be fully submitted by 11:55PM CST on the closing date. Student job openings are posted for a minimum of 3 business days.
Additional Information
All information listed in this position announcement will be used by Human Resources, the Hiring Department, and Civil Rights & Title IX for screening, interviewing and selection purposes.
Need help?
Please email the Human Resources Department at [email protected] or contact us by phone at 701-777-4226. If you anticipate needing any type of accommodation to participate in any portion of the University's employment process, including completion of the online application process, please contact our office in advance of your participation or visit.
Veteran’s Preference
Veterans claiming preference must submit all proof of eligibility by the closing date. Proof of eligibility includes a copy of NGB 22 from National Guard or Reserve (with a unit located in ND) or certification from the applicant's unit command that the individual is expected to be discharged or released from active duty in the uniformed services under other than dishonorable conditions not later than one hundred twenty days after the date of the submission of the certification. If claiming disabled status, proof of eligibility includes a DD-214 and a current letter of disability.
Confidentiality of Application Materials
Pursuant to NDCC 44-04-18.27, applications and any records related to the applications that identify an applicant are confidential, except records related to the finalists of the position, which are open to the public after the search committee has identified the top three or more finalists who will be invited to campus.
EEO Statement
The University of North Dakota is an equal opportunity employer (veteran/disability). All qualified applicants will receive consideration for employment without regard to race, color, national origin (including shared ancestry/ethnicity), religion, sex, age, disability, sexual orientation, gender identity, pregnancy or pregnancy-related condition, genetic information, marital or parental status, veteran's status, or other protected characteristic. Veterans and individuals with disabilities are encouraged to apply.
Clery Statement
In compliance with the Jeanne Clery Campus Safety Act and Campus Crime Statistics Act, the University of North Dakota publishes an Annual Security and Fire Safety Report. The report includes the university’s policies, procedures, and programs concerning safety and security, as well as three years of crime statistics for our campus. As a prospective employee, you are entitled to a copy of this report. The report and statistical data can be found online at UND.edu. You may also request a paper copy of the report from the UND Police Department located at 3851 Campus Road, Grand Forks, ND, 58202.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At University of North Dakota, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
University of North Dakota AI Hiring
University of North Dakota has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Grand Forks, ND, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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