AI Research Engineer — Representation Learning

Equifax • Alpharetta, GA

8d • Hybrid

About The Position

Equifax is seeking an AI Research Engineer to develop Transformer-based models for structured and time-series credit risk data, with a strong focus on representation learning and interpretable AI attributes. This role is ideal for a scientist who is strong in deep learning fundamentals, curious about how models learn internal representations, and excited to work on the next generation of transformer applications for structured data under senior technical mentorship. 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 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.

Requirements

• A PhD in ML/AI/CS/EE or a related quantitative field with 3+ years of relevant experience; OR an MS with 5+ years of relevant industry/research experience.
• Strong, demonstrated foundation in Transformer architectures, attention mechanisms, and sequence modeling.
• A deep, working knowledge of linear algebra, statistics, and probability—the foundational mathematics required to characterize model behavior, evaluate representation similarity (e.g., Kernel CCA), and derive insights from internal model activations.
• Experience analyzing and working with learned representations (latent spaces, embedding analysis, internal model states).
• Strong technical intuition for deep learning training dynamics—specifically regarding stability, gradient behavior, and learning rate schedules.
• Ability to write clean, well-structured, and efficient Python code.
• Demonstrated curiosity, technical ambition, and a desire to grow your research career under senior technical leadership.

Nice To Haves

• Experience with sequence-to-sequence models, diffusion models, or other generative modeling techniques.
• Experience analyzing or interpreting learned representations through techniques such as probing, attribution, or embedding visualization.
• Experience with irregular time-series, missingness handling, or temporal embedding techniques.
• Familiarity with mechanistic interpretability concepts, such as sparse autoencoders, feature dictionaries, activation analysis, or attention-pattern interpretation.

Responsibilities

• Build and experiment with transformer-based models specifically for structured and credit time-series data, pushing the boundaries of model performance and capability.
• Investigate and interpret learned representations (embeddings, latent spaces, attention patterns) to uncover how the model encodes complex financial concepts.
• Conduct ablations, hyperparameter sweeps, and controlled experiments to validate hypotheses on model behavior and training dynamics.
• Train, evaluate, and debug deep learning models using PyTorch/TensorFlow, creating high-fidelity prototypes that provide the conceptual and architectural blueprint for the engineering team.
• Partner with our internal ML Engineering team to ensure your research prototypes are successfully integrated into production pipelines.
• Work with senior technical leadership to extend our core models beyond interpretability into broader discriminative and generative modeling architectures.
• Maintain deep currency with modern deep learning techniques, including sequence-to-sequence, diffusion models, and generative approaches.

Benefits

• Comprehensive compensation and healthcare packages
• 401k matching
• Paid time off
• Organizational growth potential through our online learning platform with guided career tracks

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree