Research Scientist/Engineer, Efficient ML Systems

Core Responsibilities

• Research efficient AI/ML systems: Invent and evaluate algorithms and system techniques that improve LLM and agentic RL training and inference efficiency (memory, compute, communication, and stability).
• Scale agentic RL: Design and optimize large-scale agentic RL pipelines, including asynchronous training, experience management, reward modeling, and long-horizon stability.
• End-to-end experimentation: Design large-scale experiments spanning model architecture, training algorithms, distributed systems, and hardware-aware optimizations.
• System-aware research: Prototype research ideas directly in training and inference stacks (e.g., parallelism strategies, attention kernels, RL training pipelines) and validate them at scale.
• Promote ideas: Translate successful ideas into production-ready systems and/or publish them at top-tier conferences with full internal support.

Requirements

• Ph.D. or Master's degree in CS, AI, Systems, or related fields (Exceptional undergraduates with strong research capabilities may be considered).
• Strong foundation in LLM or large-scale ML training, including Transformers, attention mechanisms, distributed training, and optimization methods.
• Experience or strong interest in agentic RL or large-scale reinforcement learning systems, including stability, scalability, or long-horizon training challenges.
• Demonstrated interest in efficiency-focused research, such as training acceleration, memory optimization, parallelism, kernels, or RL system robustness.
• Proficient in PyTorch or JAX.
• Clean coding style and strong command of Python.
• Adaptability: A fast learner with a strong sense of responsibility, capable of wearing multiple hats and handling cross-stack challenges.

Bonus Points

• Experience deploying open-source LLMs (Qwen, DeepSeek, Kimi, GLP, Llama etc) or training custom foundation models in coding, reasoning, agent etc.
• Contributions to AI/ML systems tooling (compilers, kernels, inference runtimes) or open-source infrastructure projects.
• Background in RL, SFT, PEFT / LoRA, training data processing, evaluation, agent harnesses, sandbox environment / tool optimizations that hardens the end-to-end production AI systems.