Build benchmarks, datasets, and evaluation systems to measure and improve AI model quality for fraud, identity, and risk judgment tasks. Collaborate across research, engineering, and product to drive rigorous experimentation and iteration in high-stakes environments.
Designs and runs experiments to improve oversight of increasingly capable AI models, including model training, evaluation, and deployment of practical systems. Analyzes failures and develops techniques to train more aligned models using oversight signals.
Builds and trains large-scale multimodal agentic models involving reasoning, planning, coding, and tool calling. Requires strong ML foundations, PyTorch expertise, and experience with distributed training on massive datasets.
Research Engineer designs evaluations, studies model failures, and builds research loops to improve AI agents for high-stakes fraud detection and judgment tasks. Requires ML training experience, experimental rigor, and strong engineering skills in adversarial environments.
Builds and deploys AI primitives and agents to automate workflows and enhance user experiences in investment management platform. Requires AI agent experience, distributed systems knowledge, and product-minded engineering across tech stacks.
Leads development of proprietary AI reasoning model TRAM for interpreting global trade law, building data pipelines, fine-tuning LLMs, and evaluation frameworks for high-speed, accurate compliance determinations. Requires AI product experience, especially RAG systems and model fine-tuning.