Role overview
Equifax is looking for a Machine Learning Engineer to join our Data and Analytics Center of Excellence (D&A COE). In this role, you will serve as the critical engineering bridge between advanced AI research and robust, scalable model development. Your primary mandate is to accelerate our R&D lifecycle by engineering high-performance training pipelines and unlocking model portability across the organization.
You will partner closely with applied researchers to translate proprietary deep learning architectures into clean, modular, and reusable codebases. By leveraging distributed computing and deep learning frameworks, you will help eliminate I/O bottlenecks, maximize GPU hardware utilization during training, and transform complex financial data into standardized, easily accessible formats. Your work will directly empower teams across the organization to efficiently train, scale, and iterate on some of the most complex tabular and time-series models in the financial sector.
*Equifax has a hybrid work schedule that allows for two days of remote work (Monday and Friday) with 3 days onsite (Tuesday thru Thursday) every week.
This role reports to our office Alpharetta, GA and our Midtown (OAC, Atlanta) office may be considered.
This position foes offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.
This is a direct-hire role and is not open to C2C or vendors.
What you’ll do**
- Design and build high-throughput data pipelines (e.g., BigQuery to TFRecords) specifically engineered for distributed training and inference.
- Partner with applied data scientists to translate complex prototypes into clean, modular, and scalable production-ready code.
- Productionize machine learning models by building performant data transformations, storage, and pipelines.
- Apply development and testing best practices and demonstrate skilled software craftsmanship to produce maintainable, scalable, and quality solutions.
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
- Stay current on state-of-the-art deep learning architecture and training paradigms.
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including a cross-functional team consisting of QA, Operations, and other team members.
- Deliver on company initiatives and projects prioritized for your team and support long term technical vision.
What we're looking for
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 2-5 years of related experience
- Proficiency in Python and experience with data processing and machine learning libraries (Pandas, Numpy, Scipy, Sklearn, Tensorflow, Pytorch, etc.)
- Experience with ML models design, development or deployment
- Experience with cloud platforms and distributed computing frameworks
- Experience writing complex SQL queries and building large-scale data transformation pipelines to feed machine learning workflows.
- Experience architecting deep learning systems (TensorFlow ecosystem preferred), including custom data loading pipelines (e.g., tf.data, TFRecord serialization).
- Cloud Certification Strongly Preferred
- Advanced Framework Knowledge - Experience with deep learning framework (e.g. Tensorflow, Jax) internals, including custom training loops, subclassed Keras layers (e.g., custom attention mechanisms), and distributed training strategies (e.g., via tf.distribute).
- Large-Scale Distributed AI - Experience scaling models for distributed training and inference across multi-GPU clusters utilizing data, model, and/or tensor parallelism.
- Domain Expertise - Background in building models utilizing financial, credit, or complex time-series data
- Cloud Computing - Understand big data processing frameworks and various database technologies
- Mathematics - Understand advanced statistical concepts and machine learning algorithms
- Collaboration - Excellent verbal and written communication skills to document and present findings clearly
- Technical Leadership - Demonstrates an ability to provide guidance to colleagues
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