Senior Staff AI Engineer

About the role

The Senior Staff AI Engineer (Senior Staff Architect) will lead the design, development, and optimization of reinforcement‑learning systems for JazzX AI’s enterprise AGI platform, defining architecture, scaling training pipelines, ensuring safety and compliance, and mentoring engineers while collaborating with product, platform, and research teams.

About the company

JazzX AI leverages advanced AI to build AI‑native digital workers that transform enterprise operations, starting with regulated lending and due‑diligence workflows. The early‑stage company aims to create institutional intelligence—governed, self‑improving systems that make decisions explainable and scalable—backed by SAIGroup, a $1 B investment firm with a portfolio serving 2,000+ enterprise customers.

Requirements

• 10+ years of AI/ML engineering experience, with at least 5 years focused on reinforcement learning research and production systems.
• Proven success designing and deploying large‑scale RL architectures in enterprise settings.
• Deep expertise in RL algorithms (on‑policy PPO, A3C; off‑policy SAC, DDPG) and hands‑on experience with simulation frameworks such as OpenAI Gym, Isaac Gym, PettingZoo, or MuJoCo.
• Practical experience with multi‑agent reinforcement learning and coordination strategies for complex environments.
• Strong proficiency in Reinforcement Learning with Verifiable Rewards (RLVR) and GRPO‑like policy optimization approaches.
• Experience with test‑time compute optimization techniques, including inference‑time search, chain‑of‑thought reasoning, and adaptive computation.
• Proven ability in large language model training and fine‑tuning using both supervised and RL‑driven methods.
• Advanced software engineering skills in Python, C++, or Java, and deep knowledge of ML frameworks like TensorFlow, PyTorch, JAX, or Ray RLlib.
• Hands‑on experience with distributed training infrastructure (Kubernetes, GPU/TPU clusters, cloud ML platforms).
• Excellent communication, collaboration, and leadership abilities across multidisciplinary teams.
• PhD in Computer Science, Machine Learning, Robotics, or related field (preferred).
• Experience leading enterprise AI adoption and shaping organizational strategy for RL‑powered systems (preferred).
• Contributions to open‑source RL frameworks or publications in top‑tier conferences (NeurIPS, AISTATS, ICML, ICLR, AAAI) (preferred).
• Background in safety, alignment, or explainability of RL agents (preferred).