FICO is looking for outstanding AI scientists and engineers to build the next generation of generative AI and agentic systems for real-time, enterprise-scale decisioning. You'll design, train, and evaluate neural networks and agent systems that operate at enterprise scale within the FICO Platform, powering fraud detection, risk, and analytics for some of the world's largest financial institutions. We're looking for problem solvers who want their models to leave the lab and run in production, in one of the highest-stakes, most regulated environments in AI today.
Design, train, and evaluate neural networks and agent systems, from statistical and ML models to generative AI, LLMs, RAG, and agentic workflows, to solve real-world analytics, risk, and fraud problems for enterprise applications at massive scale.
Conduct hands-on analysis of large datasets, applying data-cleaning techniques and feature engineering to identify the right modeling approach and ensure data quality throughout
Lead and mentor teams designing and developing AI/ML solutions, partnering across FICO to integrate models into the FICO Platform under real-world time constraints.
Evaluate and integrate emerging AI technologies and frameworks, balancing performance, cost, and responsible AI standards.
Establish best practices for prompt engineering, model and agent evaluation, observability, and governance of generative AI in a highly regulated environment.
Support client and pre-sales engagements, investigating model behavior and contributing to model construction and post-implementation support of models in production.
Drive innovation by promoting new ideas and approaches that push the team and products forward.
Travel and flex your hours as business needs require.
PhD or Master's in Computer Science, Physics, Engineering, Machine Learning, Statistics, Mathematics, Operations Research, or a related technical field, with significant hands-on experience in predictive modeling, machine learning, and applied AI development.
Experience analyzing large, real-world datasets and applying data-cleaning techniques to understand their underlying structure, and translating that understanding into effective feature engineering
Deep understanding of the architectures and techniques behind generative AI and agentic systems, such as neural networks, transformers, embeddings, attention mechanisms, fine-tuning, and RAG, beyond basic familiarity with frameworks like LangChain, LangGraph, or Hugging Face.
Skilled in tuning and evaluating models: selecting and preparing training data, setting hyperparameters, and assessing performance and robustness.
Proven ability to lead cross-functional teams on medium-to-large projects and manage client interactions with strong communication skills.
Strong competency in two or more programming languages (Python, C, C++, Java) and deep learning frameworks (PyTorch, TensorFlow, JAX, DeepSpeed). Comfortable working in a Linux environment.
Strong publication record in high-impact journals or top conference proceedings is a plus.
Prior experience with real-time payment transactional data, including data field availability, timing, and format requirements for downstream analytics, is a plus.
Experience deploying AI/ML solutions within highly regulated industries (e.g., financial services, healthcare, insurance) is a plus.
