4 months ago
Base Salary
$207k - $290k/yr
Responsibilities
- Define and drive end-to-end architectures for reinforcement-learning systems, including training pipelines, simulation environments, reward shaping, and model serving.
- Apply advanced reinforcement-learning techniques to complex enterprise problems, including policy optimization, model-based RL, hierarchical RL, and multi-agent RL.
- Design distributed training systems and cloud-native infrastructure optimized for performance, reproducibility, and cost efficiency.
- Provide technical leadership, mentor AI engineers and researchers, and review designs and code for scalability and robustness.
- Partner with product, data, platform, and research teams to integrate RL solutions into production systems.
- Define benchmarking, continuous evaluation, monitoring, and feedback-driven improvement frameworks for deployed RL models.
- Ensure RL systems meet ethical AI, safety, explainability, governance, and regulatory standards.
Requirements
- 10+ years of AI/ML engineering experience, including at least 5 years specializing in reinforcement-learning research and production systems.
- Demonstrated success designing and deploying large-scale RL architectures in enterprise environments.
- Deep expertise in RL algorithms, including PPO, A3C, SAC, DDPG, RLVR, and GRPO-like policy optimization approaches.
- Hands-on experience with simulation frameworks such as OpenAI Gym, Isaac Gym, PettingZoo, or MuJoCo, and with multi-agent reinforcement learning.
- Experience with test-time compute optimization, inference-time search, chain-of-thought reasoning, and adaptive computation strategies.
- Experience training and fine-tuning large language models using supervised and reinforcement-learning techniques.
- Advanced software engineering skills in Python, C++, or Java, with deep expertise in 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 across multidisciplinary teams.
- Preferred qualifications include a PhD in Computer Science, Machine Learning, Robotics, or a related field; enterprise AI adoption leadership; open-source or top-tier conference contributions; and experience with safety, alignment, or explainability of RL agents.
Benefits
- Competitive base salary of USD 207,000 to USD 290,000 plus equity options.
- Health, dental, and vision insurance.
- Flexible working arrangements.
- Opportunity to build foundational enterprise AGI and AI systems with real-world impact.
- Headquartered in Los Altos, California.
Tech Stack
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