Interested in this AI/ML Engineer role at St. Olaf College?
Apply Now →About This Role
The Department of Mathematics, Statistics, and Computer Science (MSCS) at St. Olaf College invites applications for a full\-time, tenure track position in Statistics and Data Science at the Assistant Professor level, to begin August 2027\. We are looking for candidates who can contribute broadly to our growing statistics and data science program through teaching, research, and supervision of undergraduate research.
The normal teaching load is five courses per year. Teaching responsibilities include courses at all levels of a curriculum, from introductory courses to upper\-level courses in one’s field of expertise. Other important aspects of the position include the promotion of community\-building and inclusivity within the MSCS Department and the College, student advising, and other service work in the department and across the college.
Salary : $77,200 \- $82,000
Qualifications
Candidates must have or be completing a PhD in Statistics, Biostatistics, Data Science, or a closely related field such as Computer Science, Engineering, Applied Mathematics, Bioinformatics, or Epidemiology with a demonstrated interest and background in Statistics and Data Science. While we will consider all areas of specialization, candidates with interest/experience in Data Science who are poised to expand our course offerings at the 300\-level are particularly encouraged to apply
About the Department
Our Mathematics, Statistics, and Computer Science (MSCS) Department includes thriving programs in mathematics, statistics and data science, computer science, mathematical biology, and mathematics education. While maintaining disciplinary rigor, we promote interactions and synergy among MSCS programs and across the college. The entire department works together to provide excellent learning environments for our students. We celebrate and share multiple pedagogical approaches, we support each other in wisely stewarding departmental resources, and we meet regularly to make decisions together. Furthermore, our department is committed to the work of Diversity, Equity, and Inclusion, and we seek a tenure\-track colleague who will contribute to inclusive excellence, engage undergraduates in innovative research, and teach a variety of courses across our Statistics and Data Science curriculum.
Possibilities abound for collaborative research; projects involving undergraduates are supported by St. Olaf’s Collaborative Undergraduate Research and Inquiry program. Additionally, our Center for Interdisciplinary Research is a nationally recognized program that pairs MSCS faculty and students with faculty and students from other disciplines to share in the excitement and challenge of working across the traditional academic boundaries to collaborate on research. The St. Olaf mathematics program is among the largest and most successful at American undergraduate institutions, annually graduating about 75 mathematics majors. The statistics and data science program is also vibrant; as a new major, its inaugural class (2028\) is slated to graduate over 50 students. The computer science program is growing quickly, doubling the number of majors (from the 20’s to the 40’s) in just a few years. St. Olaf is among the nation’s top undergraduate origins of PhDs in the mathematical sciences. For more information about the department, visit wp.stolaf.edu/mscs .
We strive to be a campus of welcome where students, faculty, and staff thrive by bringing their full humanity—gender identity, sexuality, race, ethnicity, national origin, socioeconomic class, disability, religion, spirituality, and age—to St. Olaf each day. Our goal is to generate conversations and processes that over time create greater clarity, transparency, trust, cooperation, consensus, respect, and measurable outcomes. Practices that support this goal include listening, cultivating a growth mindset, respecting those with different views, being informed by data, and understanding that the work is ongoing, collaborative, organic, and ever evolving. We encourage applicants to familiarize themselves with our Community and Belonging website to learn more about our commitment and to identify how you might contribute to these efforts.
How to Apply
Throughout your materials, include how you would contribute to the development of a diverse and inclusive learning community at our college through your teaching, research, and/or service. A complete application includes the following:
*References* : Provide contact information for three professional references who can speak to your abilities in teaching and scholarship. Letters will be solicited from references by the search committee for candidates who advance to the interview stage.
*Supporting Documents and URLS* :
Cover letter (
no more than 3 pages) that provides an executive summary of your:
- specific interest in joining the MSCS Department at St. Olaf College;
+training and experience in Statistics, particularly if you do not have a Ph.D. in Statistics or Biostatistics;
+experience in and vision for diversity, equity, and inclusion initiatives in teaching, scholarship, service, and/or community engagement;
+experience in and vision for teaching undergraduate students; include experience using inclusive pedagogy, active and innovative teaching strategies, and assessment of learning outcomes as applicable; and
+
+ contributions to the scholarly field and future research goals.
- Graduate transcripts (unofficial)
- Teaching Statement: Statement of your teaching philosophy, including a discussion of pedagogical techniques used to create an active, inclusive classroom
- Professional Statement: Statement of your research and interests, including the potential for engagement in research with undergraduates, that is written for a general statistics and data science audience
Review of applications will begin on October 1, 2026, and will continue until the position is filled. Applications received by that date will receive fullest consideration. Finalist interviews are expected in November. Questions about the position, department, or College may be directed to Prof. Katie Ziegler\-Graham, Search Committee Chair, at [email protected] .
Join Our Community \- Work at St. Olaf!
Founded in 1874, St. Olaf College is a residential, coeducational liberal arts college with approximately 3,000 students and 800 faculty and staff. The college is located on a picturesque 300\-acre campus in Northfield, Minnesota, a vibrant, historic river town of 20,000 located 45 minutes south of culturally rich and diverse Minneapolis and St. Paul.
The college offers an academically rigorous, nationally ranked liberal arts education that fosters the development of the whole person in mind, body, and spirit and emphasizes learning in an inclusive and globally engaged community. We encourage applications from candidates committed to multicultural understanding and the enrichment of our diverse community.
The college offers a comprehensive benefits package, including a 9% retirement match, contributions to eligible employees' health savings accounts, a significant tuition discount (up to a 90% reduction) at ACM, GLCA, and ELCA colleges and universities for employees' children, and generous paid time off. For a full review of the college's benefits, please see the summary of our benefits here: https://wp.stolaf.edu/hr/benefits/
To provide a safe and secure educational environment, St. Olaf College verifies the accuracy of all credentials presented by applicants and conducts a criminal background check on every new hire.
A summary of the Flexible Work Policy for staff: https://wp.stolaf.edu/hr/flexible\-work\-policy/
A link to our Community and Belonging page: https://wp.stolaf.edu/equity\-inclusion/
A virtual campus tour: https://www.stolaf.edu/multimedia/play/?p\=483
An overview of Northfield: https://wp.stolaf.edu/admissions/visit/northfield/
An overview of the Twin Cities: https://wp.stolaf.edu/admissions/visit/twincities/
*For Staff Application Assistance* :
507\-786\-3068
*For Faculty Application Assistance* :
507\-786\-3356
Nondiscrimination PolicySt. Olaf College does not discriminate on the basis of sex, race, color, creed, national origin, gender identity, gender expression, sexual orientation, age, religion, disability, marital status, veteran status, or status with regard to public assistance. St. Olaf College prohibits discrimination and harassment based upon these and any other legally protected status in any education program or activity that it operates, including in admissions and employment.
Inquiries about this nondiscrimination policy may be referred to St. Olaf College's Director of Equal Opportunity, the U.S. Department of Education’s Office for Civil Rights, or both. St. Olaf's Director of Equal Opportunity (who serves as the College's Title IX, Title VI, and Section 504 Coordinator) is Pamela McDowell, Tomson Hall, [email protected] , (507\) 786\-3465\.
Salary Context
This $77K-$82K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 St. Olaf College, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($79K) sits 56% below the category median. Disclosed range: $77K to $82K.
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.
St. Olaf College AI Hiring
St. Olaf College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Northfield, MN, US. Compensation range: $82K - $82K.
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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