AI Research Engineer — Representation Learning

What You Will Do

• Advance Experimental Research: Build and experiment with transformer-based models specifically for structured and credit time-series data, pushing the boundaries of model performance and capability.
• Analyze Internal Representations: Investigate and interpret learned representations (embeddings, latent spaces, attention patterns) to uncover how the model encodes complex financial concepts.
• Execute Rigorous Experimentation: Conduct ablations, hyperparameter sweeps, and controlled experiments to validate hypotheses on model behavior and training dynamics.
• Develop Research Prototypes: 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.
• Collaborate on Integration: Partner with our internal ML Engineering team to ensure your research prototypes are successfully integrated into production pipelines.
• Drive Strategic Expansion: Work with senior technical leadership to extend our core models beyond interpretability into broader discriminative and generative modeling architectures.
• Stay at the Vanguard: Maintain deep currency with modern deep learning techniques, including sequence-to-sequence, diffusion models, and generative approaches.

What Experience You Need

• Education & Experience: 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.
• Deep Learning Foundations: Strong, demonstrated foundation in Transformer architectures, attention mechanisms, and sequence modeling.
• Mathematical Maturity: 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.
• Representation Learning: Experience analyzing and working with learned representations (latent spaces, embedding analysis, internal model states).
• Training Intuition: Strong technical intuition for deep learning training dynamics—specifically regarding stability, gradient behavior, and learning rate schedules.
• Programming Rigor: Ability to write clean, well-structured, and efficient Python code.
• Soft Skills: Demonstrated curiosity, technical ambition, and a desire to grow your research career under senior technical leadership.

What Could Set You Apart

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