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About This Role
Nelnet is a diversified and innovative company committed to enriching lives through the power of service as a student loan servicer, professional services company, consumer loan originator and servicer, payments processor, renewable energy solutions, and K\-12 and higher education expert. For over 40 years, Nelnet has been serving its customers, associates, and communities.
The perks of working at Nelnet go beyond our benefits package. When you join the Nelnet team, you're part of a community invested in the success of each individual. That support comes through in our work, as we are united by our mission of creating opportunities for people where they live, learn, and work.
At Nelnet, we believe in the transformative power of data and technology to drive business innovation. Our AI / ML Engineer role sits at the center of that work: building, operating, and scaling the AI agents that automate processes, surface insights, and help our teams make better decisions faster. If you're passionate about taking agentic systems from prototype to dependable production capability, we invite you to join our team and help shape how AI gets used across our business.
This posting covers multiple levels within the AI / ML Engineer role. We are currently seeking candidates at the equivalent of a Level I or II AI / ML Engineer. The responsibilities and qualifications below describe the full scope of the role; expectations for depth, autonomy, and scope of ownership will vary by level. Level and corresponding compensation are determined during the interview process based on demonstrated experience. We encourage you to apply if you meet the core qualifications, even if you don't match every item listed.Role Overview
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As an AI / ML Engineer at Nelnet, you'll be at the intersection of applied AI and software engineering. Your primary focus will be twofold: keeping our existing fleet of deployed agents healthy, evaluated, and improving over time, and designing new agentic capabilities that extend what those systems can do. Beyond building, you'll help expand the agentic approach across the organization, partnering with business stakeholders to identify high\-value use cases, establishing reusable patterns and guardrails, and raising the bar for how teams evaluate and ship AI systems. You'll collaborate closely with Data Scientists, Product, Software Engineers, and business partners to move solutions from concept to production.
Job Responsibilities
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1\. Agent Development: Design, build, and deploy LLM\-powered agents to solve concrete business problems. This includes tool use, multi\-step reasoning, orchestration, and human\-in\-the\-loop patterns.
2\. Operating the Existing Fleet: Own the day\-to\-day health of agents already in production: monitor behavior, diagnose failures, tune prompts and tooling, and manage model and dependency upgrades without regressing quality.
3\. Evaluation: Build and maintain evaluation suites for agent systems, including offline test sets, LLM\-as\-judge scoring, regression testing, and online metrics.
4\. Observability and Monitoring: Instrument agents end to end, including traces, tool calls, token usage, latency, cost, and outcome quality and act on what the data shows.
5\. Context and Retrieval Engineering: Design retrieval and context strategies (RAG, structured data access, caching, chunking, ranking) that give agents the right information at the right time.
6\. Tooling and Integrations: Build and maintain the tools, APIs, and connectors agents rely on, ensuring safe and reliable interaction with internal systems and data.
7\. Scalability and Infrastructure: Design and implement scalable AI pipelines and services on AWS, using infrastructure as code (Terraform) and CI/CD to automate deployment and maintenance.
8\. Guardrails and Responsible AI: Implement safety controls, input/output validation, access boundaries, and audit trails appropriate to a regulated environment.
9\. Expanding Agentic Adoption: Partner with teams across the organization to identify where agents add real value, prototype quickly, and turn one\-off wins into reusable frameworks and standards.
10\. Documentation: Maintain clear documentation of agent architectures, prompts, tool contracts, data flows, evaluation results, and known limitations.
11\. Innovation: Track the fast\-moving foundation model and agent tooling landscape, and bring what's genuinely useful into our stack.
12\. Mentorship: Provide guidance to team members on agent design, evaluation practices, and applied AI best practices.
Key Competencies
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1\. Strong programming skills in Python.
2\. Working knowledge of AWS services for AI workloads (e.g., Bedrock, Lambda, ECS/Fargate, S3, OpenSearch, Step Functions).
3\. Familiarity with agent frameworks and orchestration tooling, and sound judgment about when a framework helps versus when to build directly.
4\. Practical prompt and context engineering skill, with an evaluation\-driven approach to improving them.
5\. Experience with infrastructure as code (IaC) tools like Terraform.
6\. Proficiency using CI/CD pipelines to automate testing and deployment of AI workflows.
7\. Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
8\. Solid grounding in machine learning fundamentals and statistical reasoning, sufficient to evaluate systems rigorously and know when a non\-LLM approach is the better answer.
9\. Excellent problem\-solving skills and the ability to work in a collaborative environment.
10\. Strong critical thinking, analytical, and quantitative problem\-solving ability.
11\. Strong organization, time management, and coordination skills to drive projects to completion.
12\. Ability to communicate AI capabilities and limitations clearly to non\-technical stakeholders.
13\. Ability to lead end\-to\-end development of new products.
*Nelnet believes in a hybrid work environment that accommodates both in\-office and remote work. This model promotes a positive work\-life balance and culture, enabling in\-person collaboration when possible while also providing benefits associated with remote work. The standard hybrid work schedule includes a 24/16 hour* *(in\-office/work\-from\-home)* *split for associates that reside within 30 miles of an office. This is subject to change, based on manager discretion.*
At this time, we are unable to consider external candidates that reside in these states: Alabama, California, Connecticut, Hawaii, Illinois, Maine, Massachusetts, Michigan, New Jersey, New York, Oregon, Rhode Island, Vermont, Washington.
Qualifications
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- Bachelor’s or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field (or equivalent experience).
- U.S. Citizenship AND the ability to obtain a U.S. 6C Security Clearance.
- Minimum of 2 years of experience in machine learning engineering, AI engineering, software engineering, or related roles.
- Demonstrated experience building agentic systems with large language models. Examples include tool calling, orchestration, multi\-step workflows \- not just single\-turn prompting.
- Hands\-on experience evaluating LLM and agent systems, including designing eval sets and interpreting results to drive iteration.
- Experience deploying and supporting AI or ML systems in production environments.
- Experience with retrieval\-augmented generation and other approaches to grounding models in enterprise data.
Starting Salary Range for this Role: $95k \- 130k
Our benefits package includes medical, dental, vision, HSA and FSA, generous earned time off, 401K/student loan repayment, life insurance \& AD\&D insurance, employee assistance program, employee stock purchase program, tuition reimbursement, performance\-based incentive pay, short\- and long\-term disability, and a robust wellness program. Click here to learn more about our benefits: Benefits \& Perks \- Nelnet Inc.
Nelnet is committed to providing a welcoming and respectful workplace where all associates have the opportunity to succeed. As an Equal Opportunity Employer, we ensure that all qualified applicants are considered for employment. Employment decisions are made without regard to race, color, religion/creed, national origin, gender, sex, marital status, age, disability, use of a guide dog or service animal, sexual orientation, military/veteran status, or any other status protected by federal, state, or local law. We value the unique contributions of every team member and believe that a positive work environment benefits everyone.
Qualified individuals with disabilities who require reasonable accommodations in order to apply or compete for positions at Nelnet may request such accommodations by contacting Corporate Recruiting at 402\-486\-5725 or [email protected].
Nelnet is a Drug Free and Tobacco Free Workplace.
Use of Artificial Intelligence in Hiring
We may use automated or artificial intelligence enabled tools to assist with the initial review of applications, such as identifying relevant skills or experience. These tools are used to support human review and do not make hiring decisions. A recruiter reviews applications and determines which candidates move forward in the hiring process. For more information, see our Privacy Policy and Pre\-Use Notice: Automated Tools in Hiring
Salary Context
This $95K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nelnet, 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. This role's midpoint ($112K) sits 48% below the category median. Disclosed range: $95K to $130K.
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.
Nelnet AI Hiring
Nelnet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lincoln, NE, US. Compensation range: $130K - $130K.
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.
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