Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence

Define and lead scientific initiatives in one or more areas eg foundation models, behavioral modeling, anomaly detection, threat modeling, agentic systems. Develop scalable learning systems that understand entities, content, relationships, and behavior over time while identifying known, emerging, and previously unseen risks. Develop methods to model and propagate uncertainty across individual models, model cascades, agent trajectories, retrieved evidence, automated decisions, and human judgments. Use uncertainty, confidence, severity, and business impact to determine when to automate, gather additional evidence, invoke a more capable system, abstain, or escalate to expert review. Translate threat models and adversarial insights into data strategies, learning objectives, model architectures, agent capabilities, and evaluation plans. Advance the training, post-training, and evaluation of agents that use tools and evidence to investigate complex cases and produce grounded outcomes. Address challenging learning settings involving distribution shift, sparse or delayed labels, noisy supervision, class imbalance, selective observation, and adaptive adversaries. Provide technical leadership, mentor scientists, and influence the long-term architecture of AI-driven trust and safety systems. Bachelor's, Master's, or Doctorate degree in Computer Science, Mathematics, Statistics, Electrical Engineering, Operations Research, or a related quantitative field, with relevant industry or research experience . Strong foundation in probability, statistics, linear algebra, optimization, numerical methods, experimental design, and statistical decision theory. Deep expertise in modern machine learning, including foundation or representation learning, behavioral and temporal modeling, anomaly detection. Proven experience in post-training and evaluating large-scale models (xxx B param) Experience modeling uncertainty in production decision systems. Ability to model threat and abuse scenarious. Strong programming skills in Python and experience with frameworks such as PyTorch, JAX, TensorFlow, or equivalent technologies. Proven ability to take scientific ideas from formulation through experimentation, production deployment, and measurable impact. Demonstrated technical leadership through scientific direction, architecture, mentorship, and influence across science, engineering, product, and security teams. Experience with tool-using agents, retrieval, agent post-training, reward modeling, or trajectory evaluation. Experience in trust and safety, fraud, abuse, cybersecurity, moderation, account integrity, or policy enforcement. Experience working with temporal, multimodal, heterogeneous, or graph-structured data. Strong publication or production track record in machine learning, agents, anomaly detection, probabilistic modeling, adversarial ML, multimodal learning, or trust and safety.