Senior Staff AI Engineer
JazzX AIAbout the role
Headquartered in Los Altos, CA. Backed by SAIGroup.
Learn more about JazzX AI:
Website: https://jazzx.ai
LinkedIn: https://www.linkedin.com/company/jazzx-ai
Learn more about SAIGroup:
Website: https://saigroup.ai
About the Role
We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be responsible for shaping the vision, architecture, and technical execution of RL-driven AI reasoning models and systems that power next-generation enterprise AGI platform.
You will lead the design, development, and optimization of cutting-edge RL solutions, from experimentation and simulation through production deployment. This includes building scalable training architectures, architecting multi-agent and hierarchical RL frameworks, and ensuring that the RL systems are resilient, efficient, explainable and safe.
As a senior technical leader, you will partner with cross-functional teams—including product, core platform engineering, and research—to define architectural best practices, establish governance standards, and enable seamless integration of RL into our broader AGI platform. You will also drive innovation by exploring novel RL techniques, mentoring engineers and researchers, and ensuring the RL infrastructure can scale to support high-throughput training and real-world scenarios and enterprise use cases end to end.
Ultimately, your work will be critical in bridging research and production, ensuring that the latest RL advancements translate into reliable, impactful, and enterprise-ready AI solutions.
Key Responsibilities
- Architecture & Design: Define and drive the end-to-end architecture for reinforcement learning–based systems, including training pipelines, simulation environments, reward shaping, and model serving.
- Research & Development: Apply cutting-edge RL techniques (policy optimization, model-based RL, hierarchical RL, multi-agent RL, etc) to solve complex enterprise problems.
- Scalability & Infrastructure: Design distributed training systems, leverage cloud-native infrastructure, and optimize for performance, reproducibility, and cost-efficiency.
- Leadership & Mentorship: Provide technical leadership to AI engineers and res
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