Member of Technical Staff [Research]

Responsibilities

• Lead research initiatives in the areas of LLM reasoning, post-training, and agentic system design.
• Develop new methods to improve capability, reliability, and safety of autonomous LLM agents in real-world environments.
• Collaborate with software and platform engineers to prototype and productionize research outcomes into sticky product experiences.
• Design and execute experiments, benchmark performance, and analyze model behaviors to identify failure modes and opportunities.
• Stay abreast of emerging work in reasoning, multi-agent systems, RLHF, tool use, and LLM fine-tuning — and contribute to publications or open-source efforts where appropriate.
• Help shape the research culture and technical roadmap of the company as an early member of the team.

Qualifications

• Strong background in machine learning, natural language processing, or AI systems, with experience in large language models (LLMs).
• Deep understanding of one or more of the following areas: Agentic system design (tool-use, planning, reasoning, computer-use), LLM post-training (instruction tuning, RL, reasoning), Data pipeline design and model evaluation.
• Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow).
• Demonstrated ability to design, execute, and analyze research experiments — from idea to implementation.
• Strong communication skills and ability to work collaboratively in a fast-paced, cross-disciplinary environment.
• Nice to have:
• Experience working with open-weight models (e.g., Llama, Mistral, or similar) and training infrastructure.
• Publications in top-tier AI venues (NeurIPS, ICLR, ICML, ACL, etc.).
• Prior experience building research prototypes into usable tools or products.