We are looking for an Applied Research Engineer for the Risks Team to create state of the art foundational deep learning models for fintech. We work with Python, Pytorch, MLFlow, AWS, Snowflake, Claude Code / OpenAI Codex / Cursor.

Challenges that await you:

• Train self-supervised models on discrete sequences to beat the SOTA and achieve business impact in downstream tasks across PLATA, such as: credit risk, transaction anti-fraud and LTV forecasting.
• Stay on top of SotA research, applying the latest NLP and DL techniques to fintech models.
• Work with large multimodal datasets: tabular, behavioral, transactional, device and network, text, time series, graphs.
• Optimize the utilization of compute resources for both training and inference.
• Own and develop solutions end-to-end, from idea to data collection to experiments to training runs to inference optimization to evaluating impact.
• Perform rigorous evaluations.
• Write articles and speak at industry conferences.

What makes you a great fit:

• Solid theoretical knowledge of machine learning, deep learning and NLP.
• Demonstrated experience in training and developing modern deep learning models.
• Understanding and experience with dataset design and data mixing for DL training.
• Demonstrated understanding of how to maximize the utilization of compute resources and efficiently perform experiments to validate research ideas.
• Solid communication skills.
• High autonomy and ownership.
• Ability to work even if Claude is down

Your bonus skills:

• PhD, publications and other previous research experience.
• Experience training 1b+ transformer models.
• Experience with transformer-based architectures for non-natural language event sequences: CoLES, BERT4Rec, SASRec.
• Experience training self-supervised, embedding and ranking DL models.
• Experience in fintech and banking.
• B1 or higher English level for effective communication with an international team.