Interested in this AI/ML Engineer role at Equifax?
Apply Now →About This Role
- Atlanta / Alpharetta
- United States of America
- Technology
- Full time
- 7/20/2026
- J00178091
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Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you.
Equifax is excited to add a Machine Learning Engineer to our team.
What you’ll do
- Design complex systems of systems for training and running machine learning models with industry best practice
- Define projects and scope for teams of engineers and guide their completion
- Develop, identify, and report intellectual property through patent applications, invention disclosures, white papers, and presentations
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including executives, customers, and peers from other businesses and institutions
- Contribute to all phases of product development and delivery from Analysis \& Design all the way through to successful Deployment
- Deliver on company initiatives and prioritize projects supporting your long term technical vision
- Collaborate with the product team, architects, and others to understand the opportunities and limitations of AI, ML, and data engineering
- Participate in peer design and code reviews
- Show initiative to identify and drive forward improvements and innovations that add value and move the IT organization forward
- Elevate the performance of colleagues through training, mentoring, and promoting best practices; may function as a team lead
What experience you need
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 7\+ years of related work experience, including proven experience leading a team of MLE, DS, SDE, DevOps, or related roles
- Experience with end\-to\-end development of ML models, from ideation to deployment, ensuring best practices, scalability and reliability
- Cloud Certification Strongly Preferred
What could set you apart
- *Application Development/Programming* \- Ability to review code for quality, performance, and efficiency, and optimize critical parts of the codebase; Ability to establish the best practices of Software Development Life Cycle for the team
- *Artificial Intelligence* \- Designing scalable and maintainable machine learning architectures and frameworks for the organization's products and services; Ability to define the technical vision and roadmap for the MLE team aligned with the organization's goals and industry trends
- *Big Data Analytics* \- Deep understanding of the domain or industry in which the machine learning solutions are being applied, enabling the company to develop impactful big data solutions
- *Cloud Computing* \- Proficiency in data architecture design, data strategy development, data orchestration, data integration, ETL development, data modeling, parallel processing and performance optimization.
- *Collaboration* \- Being able to engage with internal stakeholders, including data scientists, business leaders, product managers, and executives, to understand requirements and present technical solutions; Ability to collaborate with other teams, such as software engineering, data engineering, and business intelligence, to integrate machine learning solutions into larger systems.
- *Mathematics* \- Ability to read and comprehend research papers in latest machine learning field, and applying innovative techniques to real\-world problems
- *Technical Leadership* \- Be able to lead and manage a team of machine learning engineers, data scientists, or related roles. Ability to set clear goals, provide guidance, and foster a collaborative and productive team environment
We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.
Are you ready to power your possible? Apply today, and get started on a path toward an exciting new career at Equifax, where you can make a difference!
Who is Equifax?
At Equifax, we believe knowledge drives progress. As a global data, analytics and technology company, we play an essential role in the global economy by helping employers, employees, financial institutions and government agencies make critical decisions with greater confidence.
We work to help create seamless and positive experiences during life’s pivotal moments: applying for jobs or a mortgage, financing an education or buying a car. Our impact is real and to accomplish our goals we focus on nurturing our people for career advancement and their learning and development, supporting our next generation of leaders, maintaining an inclusive and diverse work environment, and regularly engaging and recognizing our employees. Regardless of location or role, the individual and collective work of our employees makes a difference and we are looking for talented team players to join us as we help people live their financial best.
Equifax is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics.
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 Equifax, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Equifax AI Hiring
Equifax has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
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.
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