About the Role
As a Member of Technical Staff [Research] at NeoCognition, you’ll be part of the core team advancing the frontier of LLM agents — systems that can reason, plan, and act reliably in the real world. We are an AI research lab focused on making LLM agents reliable, grounded, and accessible to users, developers and enterprises.
You’ll lead end-to-end research projects — from conceptualization and experimentation to building and testing product prototypes — working closely with our engineers and designers to turn cutting-edge ideas into practical, impactful systems.
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
Required
- 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.