Teams at Weekday, Inc.

Build and evaluate MLOps and ML systems tasks for frontier AI training data. Responsibilities include designing technical challenges, writing solutions and evaluation rubrics, profiling and optimizing GPU workloads, debugging distributed systems, improving model performance, and supporting high-throughput LLM inference. The role requires production experience with ML infrastructure, serving systems, GPU accelerators, JAX or PyTorch, and strong technical communication.

Design insurance pricing, reserving, forecasting, and portfolio-risk scenarios for AI model training. Create actuarial analyses and reference answers, evaluate AI outputs for quantitative and methodological accuracy, identify unsupported assumptions and errors, and provide structured feedback. The role requires explaining actuarial methods and business implications to technical and nontechnical audiences while participating in calibration and onboarding sessions.

Design reinsurance and program-business scenarios, assess AI-generated responses, create market-quality reference materials, and provide detailed feedback on treaty structures, delegated authority, fronting, captives, risk transfer, contract logic, financial responsibilities, and market realism.