Interested in this AI/ML Engineer role at Nelnet?
Apply Now →Skills & Technologies
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.
We need someone who's equally comfortable in security engineering and AI technology, and who's ready to work closely with IT, security, and data science teams to build AI solutions that are secure, compliant, and built to last.
This role calls for real technical depth, sound judgment, and a proactive approach to risk management. Here's what you'll be doing:
- Monitoring and strengthening the security posture of our AI/ML systems, APIs, and model\-serving environments
- Building detection and monitoring capabilities to identify risks such as model misuse, prompt injection, data poisoning, and unauthorized model access
- Partnering with development, operations, and security teams to secure AI environments across their full lifecycle
- Automating security monitoring and remediation workflows for AI systems
- Evaluating and implementing AI security tools and model governance solutions
- Contributing to the development of AI\-specific risk frameworks, controls, and policies
- Staying current on the evolving AI risk landscape and supporting security assessments and testing of LLMs and AI services
AI and Security Engineering
- Secure AI/ML model development and deployment environments, including LLMs, vector databases, and training pipelines.
- Build automated tools to detect and mitigate AI\-related risks, such as anomalous model outputs and prompt injection attempts.
- Conduct security testing and threat modeling for AI systems.
- Support logging, monitoring, and alerting for AI/ML environments integrated with SIEM/XDR platforms.
- Help design model access controls, encryption, and governance enforcement.
- Assist in developing and enforcing AI\-related security policies and procedures.
Innovation
- Question legacy assumptions and recommend AI\-native security approaches.
- Identify short\- and long\-term strategies for securing AI assets in ways that support business value.
- Build security prototypes and proof\-of\-concepts for emerging AI architectures.
- Stay current on AI security research and turn findings into practical defenses.
Agility
- Adapt as the AI landscape and threat environment continue to evolve.
- Navigate ambiguity in AI risk management and compliance requirements.
- Help business and technical partners adjust to changing AI security requirements.
- Stay responsive and adaptive when incidents involving AI systems arise.
Problem Solving
- Investigate security incidents involving AI\-generated outputs or manipulated inputs.
- Apply critical thinking to defend against emerging AI risks.
- Develop layered mitigation strategies across models, APIs, and infrastructure.
- Own problem resolution from start to finish.
Collaboration
- Partner with CyberSecurity analysts, developers, and AI engineers to defend AI systems.
- Mentor peers on emerging risks and best practices in AI/ML security.
- Foster cross\-functional alignment to build AI security into enterprise roadmaps.
Communication Skills
- Translate technical AI security issues into business risk language for stakeholders.
- Write clear documentation for detection rules, playbooks, and findings.
- Present AI risk scenarios and mitigation strategies to both technical and non\-technical audiences.
Strategic Focus
- Align AI security efforts with Nelnet's business, compliance, and technology goals.
- Serve as a trusted advisor on AI governance, LLM access, and model risk.
- Anticipate future regulatory requirements around AI usage and safety.
- Deliver security solutions that balance innovation with operational integrity.
\*\*Pay Range for this role is \- $115,000 \-$155,000 dependent on experience and education.
EDUCATION:
Knowledge equivalent to completing a Bachelor's degree in Computer Science or a related field.
EXPERIENCE:
- 3 to 5 years of experience in cybersecurity, security engineering, or risk management.
- Hands\-on experience with machine learning systems, LLMs (Anthropic Claude, OpenAI, or open\-source models), or AI/ML platforms such as SageMaker, Azure ML, or Vertex AI.
- Familiarity with adversarial machine learning concepts and model risk is preferred.
- Experience with security tools, monitoring platforms, or security automation frameworks.
- Experience in application security, DevSecOps, or secure software development lifecycle (SDLC) is a plus.
COMPETENCIES – SKILLS/KNOWLEDGE/ABILITIES:
Needs:
- Knowledge of security, controls, and computer technology.
- Ability to lead and motivate others.
- Ability to apply statistics and probability to identify problems, trends, and relationships in work\-related data.
- Knowledge of at least one computer development language, along with related methodologies and techniques.
- Understanding of AI system architectures and their security implications.
- Scripting or development experience in Python or a similar language.
- Knowledge of AI\-specific risk frameworks such as MITRE ATLAS or the OWASP LLM Top 10\.
- Strong analytical skills and comfort working with data, logs, and system telemetry.
- Ability to work across teams and lead cross\-functional security initiatives.
- Familiarity with regulations governing IT environments.
- Familiarity with regulatory and ethical frameworks around AI security and model governance.
- Familiarity with enterprise LLM deployment and governance, including tools like Claude or similar platforms.
- Familiarity with infrastructure deployment and systems administration.
- Excellent organizational, presentation, verbal, and written communication skills.
- Ability to assess and communicate risk and urgency clearly to both management and engineering staff.
- Strong self\-motivation, with the ability to set and follow through on long\-term goals.
- Genuine interest in staying technically current and building new expertise.
- Comfortable questioning existing assumptions when it makes sense to do so.
- Openness to changing technology and business needs.
- Sees change as a chance to grow rather than a disruption.
- Ability to adjust communication style to fit the audience.
Wants:
- Knowledge of enterprise risk management and security governance frameworks.
- Familiarity with common security tooling and methodologies.
- Solid understanding of machine learning architectures, LLMs (GPT, LLaMA, Claude), and common AI frameworks such as PyTorch, TensorFlow, or Hugging Face.
- Please note that we are unable to provide visa sponsorship for this position. To be considered, candidates must already be authorized to work in the United States without the need for current or future sponsorship
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: LINK.
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 $115K-$155K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($135K) sits 38% below the category median. Disclosed range: $115K to $155K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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: $155K - $155K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.