Intern Researcher - Embodied AI

Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving.

• Research and develop innovative models of LLM agents (decision-making, planning, reasoning, etc) with applications on (open-world) virtual environments;

• Publish at top-tier ML conferences, file high-value patents, and write scientific technical reports;

• Contribute to the design, implementation, test, and maintenance of research and development frameworks;

• Ability to learn and grow in a collaborative research and development environment; communicate and develop solutions for complex learning problems;

• Keeping up-to-date on selected areas of ML (such as Transformers, State Space Models, and deep learning) and new advances in the NLP/AI field and bringing insights to the team.

• Ph.D. in Computer Science, Electrical and Computer Engineering, Statistics, Applied Mathematics or a related technical field (or Master’s with related research experience and publications) with 2+ years of research and development experience

• Good theoretical knowledge and methodological experience in the deep learning research-and-development cycle: literature review, model formulation and development, training, testing, monitoring, fine-tuning, and quantitative/qualitative analysis

• Proficiency in a programming language (e.g. Python); Good understanding of basic data structures and computational algorithms; strong AI/ML coding skills; experienced in deep learning/machine learning frameworks (PyTorch and Tensorflow)

• Excellent team working, technical writing and presentation, analytical and problem-solving skills

• Experience with LLMs, Transformer pipelines, and training and evaluation algorithms in NLP; related frameworks (Huggingface, DeepSpeed, PEFT)

• Good knowledge and experiences with various learning paradigms such as in-context learning, prompt tuning, and parameter efficient fine-tuning.

• Publication record at top-tier ML/AI conferences such as NeurIPS, ICML, ICLR, etc.