We’re hiring an Applied AI Researcher in the deep-tech and AI research industry.

We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions.

Key Responsibilities

• Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration
• Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains
• Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases
• Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness
• Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others
• Explore post-training optimization, fine-tuning, and domain adaptation methods
• Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions
• Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities
• Document and communicate findings through technical reports, presentations, and publications
  
  

Required Qualifications

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field
• 4+ years of experience in applied AI research or equivalent industry R&D experience
• Strong foundations in optimization, probability, and linear algebra
• Expertise in Python and frameworks like PyTorch or JAX
• Experience with RL and post-training methods (e.g., SFT, DPO, RLHF)
• Proficiency in building and aligning small language models (SLMs), including reasoning-specific models
• Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks
• Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node)
• Experience in designing evaluation metrics and performance analysis methodologies
  
  

Preferred Experience

• Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.)
• Experience applying AI to scientific domains like drug discovery, chip design, or materials science
• Exposure to multimodal models and VLMs
• Experience with open-ended systems and emergent behavior in agent-based learning
• Background in computational science (chemistry, physics, EE, or applied math)
• Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow
• Experience with domain adaptation, interpretability, and model optimization for deployment
• Contributions to open-source AI projects
• Expertise in building GPU-accelerated pipelines and optimizing inference at scale
  
  

What We Offer

• Work on high-impact, frontier research with real-world applications
• Access to high-performance computing resources
• A collaborative, intellectually stimulating environment
• Autonomy to explore novel ideas aligned with our mission
• Competitive salary, benefits, and opportunities for growth
  
  

Skills: building small language models,design,distributed training,performance analysis,pytorch,reinforcement learning,models,evaluation metrics,prompting strategies,evaluation frameworks,applied ai research,python,optimization,jax,dataset management,linear algebra,research,probability,multi-agent systems