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Advisors, Data Science \& Analytics for various \& unanticipated worksites throughout the U.S. (HQ: Chicago, IL). Lead internal teams to drive innovation initiatives and deliver long term value\-added propositions for internal customers. Independently develop new model architectures and frameworks, training methodologies, data representation, validation and evaluation, and integration of state\-of\-the\-art approaches such as generative AI. Design and write programs and scripts in various languages such as Python, SQL, R, SparQL on\-prem, and other big data computing platforms. Engage in large\-scale model training using distributed processing systems such as Ray and SPARK. Design and develop deep learning systems that can operate on tabular and graph data which requires advanced knowledge in network architectures and graph computations. Design and implement AI systems that leverage agent\-based architectures using well\-known frameworks (e.g., LangChain, Pydantic AI, DsPy), enabling dynamic, contextaware decision\-making and interaction across enterprise applications. Stay abreast of cutting\-edge research and translate academic advancements into practical solutions. Deliver analytic insights and recommendations in succinct and compelling presentations for internal stakeholders and senior management. Identify strategies and opportunities for automation. Provide mentorship and training to junior colleagues and maintain progress on all initiatives without supervision. Technical Environment: Python, SQL, R, SparQL, Ray, SPARK, LangChain, Pydantic AI, DsPy, Graph Neural Networks, Hive, PyTorch, TensorFlow, Scikit\-learn, JAX, AWS, GCP, MLOps practices and lifecycle management, Machine Learning, Kubernetes, MATLAB, data modeling, ontology development.
Job Requirements
\*Master’s degree in Computer Science, Mathematics, Statistics, or a related field plus 3 years of experience in data analytics required. Required skills: Python, SQL, R, SparQL, Ray, SPARK, LangChain, Pydantic AI, DsPy, Graph Neural Networks, Hive, PyTorch, TensorFlow, Scikit\-learn, JAX, AWS, GCP, MLOps practices and lifecycle management, Machine Learning, Kubernetes, MATLAB, data modeling, ontology development. Telecommuting Permitted. (\*Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field plus 6 years of progressive experience in data analytics also acceptable).
Benefits \& Pay Scale Information:
TransUnion provides flexible benefits including flexible time off for exempt associates, paid time off for non\-exempt associates, up to 12 paid holidays per year, health benefits (including medical, dental, and vision plan options and health spending accounts), mental health support, disability benefits, up to 12 weeks of paid parental leave, adoption assistance, fertility planning coverage, legal benefits, long\-term care insurance, commuter benefits, tuition reimbursement, charity gift matching, employee stock purchase plan, 401(k) retirement savings with employer match, and access to TransUnion’s Employee Resource Groups. Spousal, domestic partner, and other eligible dependent coverage is available on select health and welfare plans.
The U.S. base salary range for this position is $142, 210/yr \- $144, 244/yr \+benefits (www.transunion.com/about\-us/careers/life\-at\-tu) annually. Regular, fulltime non\-sales positions may be eligible to participate in TransUnion’s annual bonus plan. Certain positions may also be eligible for long\-term incentives and other payments based on applicable company guidance and plan documents.
We are committed to being a place where diversity is not only present, it is embraced. As an equal opportunity employer, all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability status, veteran status, genetic information, marital status, citizenship status, sexual orientation, gender identity or any other characteristic protected by law.
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Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, Fair Chance Initiative for Hiring Ordinance, and the California Fair Chance Act.
Adherence to Company policies, sound judgment and trustworthiness, working safely, communicating respectfully, and safeguarding business operations, confidential and proprietary information, and the Company’s reputation are also essential expectations of this position.
Company:
TransUnion LLC
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At TransUnion, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
TransUnion AI Hiring
TransUnion has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Remote, US, Chicago, IL, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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