Develop labeling systems that support dataset segmentation, coverage analysis, quality filtering, training-data mixtures, and model performance attribution.
Build the agentic-security surface. Partner on red-teaming and adversarial evaluation - tool-chain exploits, RBAC and permission-inheritance failures, multi-turn safety erosion - so we catch what a motivated adversary would.
Bachelor's degree or above in Computer Science or related fields, with 3+ years of engineering experience in at least one of Python, TypeScript, Java, or C/C++.
Domain expert: You've scaled AI products in production and design for LLM failure modes such as hallucination, context degradation, prompt injection and latency under load.
Conduct reinforcement learning (RL) training experiments for long-horizon tasks, including hyperparameter tuning, performance analysis, and iterative optimization.
Hands-on Technical Leadership: Stay technically involved and step in when needed to write code, debug issues, review architecture and resolve complex engineering problems.
Work directly with the CEO, who has built and run multiple award-winning companies across several industries over 17 years. One decision maker, no committee. Your job includes challenging the brief where the brief is wrong, and you will get a straight answer either way, quickly.