Über
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 Alpharetta, GA office, and our Midtown (OAC, Atlanta) office may be considered.
This position does not 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 Experience You Need
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
What Could Set You Apart
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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Sprachkenntnisse
- English
Hinweis für Nutzer
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