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Member of Technical Staff, LLM Research
Accepting applicationsRecruitSeq · Palo Alto, CA
Full-Time Mid_senior AIPythonaiatedeep learning
Posted
3d ago
Category
Test
Experience
Mid_senior
Country
United States
Member of Technical Staff, LLM Research
Palo Alto, CA (On-Site M-F)
Our client is an early AI research lab focused on advancing reliable, grounded LLM agents for real-world use across consumers, developers, and enterprises. Backed by leading investors, the team has previously shipped research used in frontier models at major AI labs.
About the Role
As a Member of Technical Staff on the research team, you will lead end-to-end projects that push the frontier of LLM agents - from new reasoning and post‑training methods to agentic system designs that work reliably in production. You will take ideas from concept to experimental validation to product prototypes, working closely with engineering and design to turn research into practical, high‑impact systems deployed in real-world environments.
Responsibilities
Lead research initiatives in LLM reasoning, post‑training, and agentic system design, with a focus on real‑world reliability and grounding.
Develop and evaluate new methods to improve the capability, robustness, and safety of autonomous LLM agents operating in complex environments.
Design, run, and analyze experiments and benchmarks to characterize model behavior, uncover failure modes, and identify opportunities for improvement.
Collaborate with software and platform engineers to prototype, iterate on, and productionize research into sticky, user‑facing product experiences.
Stay current on emerging work in reasoning, multi‑agent systems, RLHF, tool use, and LLM fine‑tuning, and incorporate relevant advances into the roadmap.
Contribute to publications, open‑source efforts, and internal research standards while helping to shape the research culture as an early technical hire.
Qualifications
1+ years of experience in LLM research focused on agents, post‑training, or evaluations, with a track record of publications or equivalent work.
PhD in a relevant field along with quality publications.
Strong background in machine learning or related fields, with depth in at least one of: RLHF, reasoning, multi‑agent systems, or tool‑using agents.
Proficiency in Python and modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Experience designing and executing rigorous experiments, benchmarks, and analyses on LLMs or large‑scale models.
Ability to collaborate closely with product and engineering teams to translate research ideas into prototypes and production systems.
Willingness to work on‑site 5 days per week in the Palo Alto area; open to relocation.
Preferred Skills
Experience with open‑weight LLMs and post‑training techniques (instruction tuning, reinforcement learning, or reasoning‑focused training).
Prior work on agentic system design, multi‑agent systems, or AI coding tools.
Research or engineering experience at early‑stage AI startups, research labs, or top‑tier industry labs.
Strong record of impactful research through publications, open‑source contributions, or widely used internal systems.
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Palo Alto, CA (On-Site M-F)
Our client is an early AI research lab focused on advancing reliable, grounded LLM agents for real-world use across consumers, developers, and enterprises. Backed by leading investors, the team has previously shipped research used in frontier models at major AI labs.
About the Role
As a Member of Technical Staff on the research team, you will lead end-to-end projects that push the frontier of LLM agents - from new reasoning and post‑training methods to agentic system designs that work reliably in production. You will take ideas from concept to experimental validation to product prototypes, working closely with engineering and design to turn research into practical, high‑impact systems deployed in real-world environments.
Responsibilities
Lead research initiatives in LLM reasoning, post‑training, and agentic system design, with a focus on real‑world reliability and grounding.
Develop and evaluate new methods to improve the capability, robustness, and safety of autonomous LLM agents operating in complex environments.
Design, run, and analyze experiments and benchmarks to characterize model behavior, uncover failure modes, and identify opportunities for improvement.
Collaborate with software and platform engineers to prototype, iterate on, and productionize research into sticky, user‑facing product experiences.
Stay current on emerging work in reasoning, multi‑agent systems, RLHF, tool use, and LLM fine‑tuning, and incorporate relevant advances into the roadmap.
Contribute to publications, open‑source efforts, and internal research standards while helping to shape the research culture as an early technical hire.
Qualifications
1+ years of experience in LLM research focused on agents, post‑training, or evaluations, with a track record of publications or equivalent work.
PhD in a relevant field along with quality publications.
Strong background in machine learning or related fields, with depth in at least one of: RLHF, reasoning, multi‑agent systems, or tool‑using agents.
Proficiency in Python and modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Experience designing and executing rigorous experiments, benchmarks, and analyses on LLMs or large‑scale models.
Ability to collaborate closely with product and engineering teams to translate research ideas into prototypes and production systems.
Willingness to work on‑site 5 days per week in the Palo Alto area; open to relocation.
Preferred Skills
Experience with open‑weight LLMs and post‑training techniques (instruction tuning, reinforcement learning, or reasoning‑focused training).
Prior work on agentic system design, multi‑agent systems, or AI coding tools.
Research or engineering experience at early‑stage AI startups, research labs, or top‑tier industry labs.
Strong record of impactful research through publications, open‑source contributions, or widely used internal systems.
Show more Show less