Senior LLM Applied Researcher

Company Description

We are a fast-growing, stealth-mode startup dedicated to creating AI-powered products that turn platforms into autonomous systems, allowing users to focus on what truly matters. Our team values collaboration, innovation, and impact, while working at the forefront of cutting-edge AI research. We fuse multimodal LLMs, RAG, reinforcement learning, and edge computer vision into a breakthrough physical AI stack.

Backed by a rapidly expanding base of paying customers and strategic partners, we are seeking a senior applied LLM researcher to lead independent, end-to-end research efforts. This role offers a rare opportunity to shape core technology from the ground up and play a central role in advancing Physical AI.

Role Description

As an AI Researcher at our stealth‑mode startup, you will spearhead the creation of cutting‑edge algorithms in agentic AI and information retrieval. From pioneering early‑stage research to real‑world deployment, you will develop AI algorithms that deliver exceptional performance, efficiency, and reliability on local computers.

Partnering with fellow researchers, you’ll craft novel approaches and innovative training strategies that thrive under tight computational budgets. At the same time, you’ll collaborate with our software engineering team to deliver production-ready solutions that meet rigorous standards for quality, stability, and throughput, while minimizing model footprint and inference latency.

Qualifications

• MSc or PhD in Mathematics, Computer Science, Electrical Engineering, Physics, or a related technical field
• 6+ years of hands-on experience in Python, including libraries such as PyTorch, LangChain, LangGraph, etc.
• In-depth understanding of machine learning, deep learning, NLP and MLLM fundamentals.
• At least 4 years of experience related to deep learning models for NLP tasks, including RAG, agentic ai, training and fine-tuning models.
• Appreciation for best engineering practices, including version control, clean code, code reviews, testing, CI/CD, and automation.
• Strong self-learning and research capabilities
• Team player with strong communication and collaboration skills.
• Publications in Top-Tier scientific venues.

Advantages

• Hands-on experience in the entire life cycle of creating a production grade NLP model.
• High proficiency in building RAG and information retrieval systems and in agentic ai.
• Experience in RL.
• Experience in LLM inference optimization.
• Computer vision experience.