Senior Staff AI Engineer

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 researchers; mentor junior team members; review designs and code with a focus on scalability, robustness, and clarity.
• Collaboration: Partner with product, data, and platform teams to align RL solutions with strategic business goals and integrate them into production systems.
• Evaluation & Monitoring: Define frameworks for benchmarking, continuous evaluation, and feedback-driven improvements in deployed RL models.
• Compliance & Safety: Ensure RL systems align with ethical AI practices, safety constraints, and regulatory standards for enterprises.

Required Qualifications

• 10+ years of experience in AI/ML engineering, including at least 5 years specializing in reinforcement learning research and production systems.
• Demonstrated success in designing and deploying large-scale RL architectures in enterprise environments.
• Deep expertise in reinforcement learning algorithms, including on-policy (PPO, A3C) and off-policy (SAC, DDPG) methods, along with hands-on work in simulation frameworks (e.g., OpenAI Gym, Isaac Gym, PettingZoo, MuJoCo).
• Practical experience with multi-agent reinforcement learning (MARL), including coordination strategies for complex environments.
• Strong proficiency in Reinforcement Learning with Verifiable Rewards (RLVR) and GRPO-like policy optimization approaches, applying reinforcement learning principles both rigorously and pragmatically.
• Experience with test-time compute optimization techniques, including inference-time search, chain-of-thought reasoning, and adaptive computation strategies for improving model performance during deployment.
• Proven ability in large language model (LLM) training and fine-tuning, across both supervised and reinforcement learning–driven techniques.
• Advanced software engineering skills in Python, C++, or Java, with deep expertise in ML frameworks such as TensorFlow, PyTorch, JAX, or Ray RLlib.
• Hands-on experience with distributed training infrastructure (Kubernetes, GPU/TPU clusters, and cloud ML platforms).
• Excellent communication, collaboration, and leadership skills, with experience working across multidisciplinary teams.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Robotics, or related field.
• Experience leading enterprise AI adoption and guiding organizational strategy for RL powered systems.
• Contributions to open-source RL frameworks or publications in top-tier conferences (NeurIPS, AISTATS, ICML, ICLR, AAAI).
• Background in safety, alignment, or explainability of RL agents.