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.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
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.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
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.
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.
Collaborate with data scientists to improve feature selection, feature engineering, and enable end-to-end AI workflows from model training to deployment and monitoring.