Applied AI Scientist

As an Applied AI Researcher in the deep-tech and AI research industry, you will be part of an advanced research team focused on designing multi-agent architectures, developing domain-specific scaffolding techniques, and building evaluation frameworks for next-generation AI systems. Your work will involve combining LLMs, reinforcement learning, and multi-agent systems to create scalable and meaningful solutions at the forefront of applied AI. **Key Responsibilities:** - Architect and implement novel multi-agent systems for advanced problem-solving and collaboration - Design domain-specific scaffolding techniques to customize AI behavior for complex, real-world domains - Curate and manage high-quality datasets for training and evaluating AI models in scientific and industrial applications - Establish and improve evaluation frameworks to measure performance, alignment, and robustness - Research and apply reinforcement learning techniques such as RLHF, DPO, and GRPO - Explore post-training optimization, fine-tuning, and domain adaptation methods - Collaborate with engineering and product teams to translate research into production-ready solutions - Stay updated on cutting-edge AI developments and contribute to research communities through technical reports, presentations, and publications **Required Qualifications:** - Masters 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) and 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 - Experience in designing evaluation metrics and performance analysis methodologies **Preferred Experience:** - Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) - Experience in applying AI to scientific domains such as 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 In this role, you will have the opportunity to work on high-impact, frontier research with real-world applications, access high-performance computing resources, be part of a collaborative and intellectually stimulating environment, have autonomy to explore novel ideas aligned with the company's mission, and receive a competitive salary, benefits, and opportunities for growth.,