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About This Role
Position Title:
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MLOps Engineer
Job Group:
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Professional \& Scientific
Required Minimum Qualifications:
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Bachelor's degree
Job Description:
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Summary of Duties and Responsibilities:
Looking for an opportunity to apply AI and machine learning in ways that truly matter?
If so, the Translational AI Center (TrAC) at Iowa State University is seeking applicants for a MLOps (Machine Learning Operations) Engineer! This position will be classified as a Research and Development Engineer I.
What You'll Do:
- Build and maintain machine learning infrastructure, CI/CD pipelines, and automated data workflows.
- Implement model tracking, versioning, and reproducibility practices.
- Deploy and manage machine learning models in cloud and production environments.
- Containerize applications and support scalable deployment platforms.
- Develop and maintain APIs, applications, and services that deliver AI solutions.
- Monitor system performance, troubleshoot issues, and optimize reliability.
- Write, test, and maintain high\-quality, reusable software.
- Create and maintain technical documentation.
- Participate in code reviews and contribute to shared tools and libraries.
- Collaborate with researchers and stakeholders to transition prototypes into production systems.
- Support system integration and communicate technical requirements.
- Provide technical guidance and training as needed.
This is a two\-year, fixed term position with the opportunity for renewal.
Preferred Qualifications:
- Bachelor's degree or above in Computer Science, Computer Engineering, Software Engineering, Data Science, or a related technical field.
- Two years of related experience in backend software development, AI/ML model deployment, MLOps, DevOps, cloud/platform engineering, or a closely related technical area.
- Experience deploying AI/ML models from prototype to production, including containerization, model versioning, automated deployment, monitoring, and operational support.
- Strong backend development experience, preferably in Python, including APIs, distributed services, databases, testing, and reusable software components.
- Experience with CI/CD, Git\-based workflows, containerization, infrastructure automation, experiment tracking, observability, and secure deployment practices.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, including compute, storage, identity management, monitoring, and managed AI/ML services.
- Experience deploying and supporting shared technical platforms, including user access management, upgrades, backups, capacity planning, and user support.
About TrAC:
At the Translational AI Center (TrAC), we bridge the gap between cutting\-edge AI research and real\-world applications. Through federally funded initiatives, seed grant projects, and interdisciplinary research thrusts, we drive innovation that shapes the future of AI. To learn more about TrAC, please visit TrAC .
This posting will be open until filled. However, to receive full consideration, applications need to be submitted before August 14th, 2026\.
Application Instructions:
To apply for this position, please click on “Apply” and complete the Employment Application. Please be prepared to enter or attach the following:
Resume/Curriculum Vitae
Letter of Application/Cover Letter
Why Choose Iowa State University?
Iowa State Employees enjoy comprehensive health and work\-life benefits, including medical and dental, as well as:
- Retirement benefits including defined benefit and defined contribution plans
- Generous vacation, holiday, and sick time and leave plans
- Onsite childcare (Ames, Iowa)
- Life insurance and long\-term disability
- Flexible Spending Accounts
- Various voluntary benefits and discounts
- Employee Assistance Program
- Wellbeing program
- Iowa State offers WorkFlex options for some positions. WorkFlex offers flexibility on when, where, and how you do your work. For more information, please speak with the Hiring Manager.
If you have questions regarding this application process, please email [email protected], or call 515\-294\-4800 or Toll Free: 1\-877\-477\-7485\.
Appointment Type:
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Regular with Term Appointment (Fixed Term)
Proposed End Date or Length of Term:
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August 31, 2028
Number of Months Employed Per Year:
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12 Month Work Period
Time Type:
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Full time
Pay Grade:
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PS810
Original Posting Date:
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August 7, 2026
Posting Close Date:
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Job Requisition Number:
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R19645
Iowa State University does not discriminate on the basis of race, color, age, ethnicity, religion, national origin, pregnancy, sexual orientation, genetic information, sex, marital status, disability, or status as a U.S. Veteran. Inquiries regarding non\-discrimination policies may be directed to Office of Equal Opportunity, 2680 Beardshear Hall, 515 Morrill Road, Ames, Iowa 50011, Tel. 515\-294\-7612, email [email protected] .
General ISU compensation information can be found on the University Human Resources website. Please note that this is only a list of ranges and individuals will be paid commensurate with qualifications.
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Iowa State University, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 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.
Iowa State University AI Hiring
Iowa State University has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Ames, IA, 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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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
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