Senior Applied Scientist - Agentic AI

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 an applied LLM researcher to conduct 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. Your work will span the entire lifecycle, from initial exploratory research to practical implementation, as you develop AI solutions that ensure exceptional performance, efficiency, and reliability for control stations and edge devices functioning in real-world environments.

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 or Physics.

• Publications in Top-Tier scientific venues.

• 6+ years of hands-on experience in Python, including libraries such as PyTorch, LangChain, LangGraph, etc.

• At least 4 years of experience related to deep learning models for NLP tasks, including RAG, agentic ai, training and fine-tuning models.

• In-depth understanding of machine learning, deep learning, NLP and MLLM fundamentals.

• Appreciation for best engineering practices, including version control, clean code, code reviews, testing, CI/CD, and automation.

• Strong self-learning and research capabilities.

• Strong coding background.

• Team player with strong communication and collaboration skills.

• Proven track record of excellence.

Advantages

• Hands-on experience in the entire life cycle of creating a production grade LLM model.

• Expertise in agentic AI and the development of information retrieval and RAG style systems.

• Experience in RL.

• Experience in LLM inference optimization.

• Experience in classical machine learning, data science and and computer vision.

• Present or past security clearance.