Build and own the research platform for an AI-driven investment firm, including signal frameworks, backtesting engines, research APIs, financial data pipelines, evaluation tooling, and production trading systems. Ensure realistic simulations, data integrity, reproducibility, risk analysis, and protection against leakage and bugs. Collaborate closely with researchers and engineers, optimize numerical workloads, and manage deployment, monitoring, and reconciliation between live trading and backtests.

Build the core infrastructure for an AI-native investment research and automated trading platform. Responsibilities include experiment environments, compute orchestration, storage, observability, deployment pipelines, sandboxed agent tooling, data ingestion and versioning, backtesting, production reliability, monitoring, failure recovery, and AI-native engineering practices. Collaborate closely with researchers and quants to move ideas from experiments into deployed strategies.

Research and build autonomous multi-agent AI systems that discover, evaluate, and execute investment strategies. The role involves designing agentic tool-use and self-critique harnesses, conducting experiments, developing evaluation frameworks, creating knowledge bases, and collaborating with quantitative researchers. Candidates need strong empirical judgment, research ability, technical depth, and the initiative to turn ambiguous ideas into shipped systems and product improvements.