LLM Applied Data Scientist (RAG/ NLP)
binance
If you want to work on large language models and AI reasoning from the start of your career, this is a direct path. Binance is scaling its AI capabilities across a 300+ million user ecosystem, and they need engineers who can move fast and think clearly about model performance.
You'll work across the full machine learning lifecycle: acquiring and structuring datasets, fine-tuning models, building reward systems, and running reinforcement learning experiments. This means real exposure to modern LLM development—data curation, supervised fine-tuning, RLHF workflows, and evaluation frameworks. You'll synthesize datasets through rewriting and augmentation techniques to improve how models reason and plan.
This role fits you if you have a computer science, mathematics, or physics background with hands-on experience in machine learning, NLP, or deep learning. A portfolio showing RAG work, prompt engineering projects, or model fine-tuning attempts matters more than a perfect GPA. Comfort with Python, PyTorch, and distributed computing is expected.
The position is remote, based nominally in Taiwan. To apply, submit your resume, a brief note on why LLM reasoning interests you, and a link to your GitHub or relevant project work through CareerJumpShip.
About this role
Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world. About the Role We are seeking a highly skilled Research Scientist/Engineer to advance the reasoning and planning capabilities of large foundation models. In this role, you will enhance model performance across the entire development lifecycle—including data acquisition, supervised fine-tuning (SFT), reward modelling, and reinforcement learning—while driving innovations in reasoning and decision-making. You will synthesise large-scale, high-quality datasets through rewriting, augmentation, and generation techniques to strengthen foundation models during pretraining, SFT, and RL stages. A key part of the role involves solving complex tasks using System 2 thinking and applying advanced decoding strategies such as MCTS and A*. You will design and implement robust evaluation methodologies, teach models to interact with external tools, APIs, and code interpreters, and build agents and multi-agent systems capable of addressing sophisticated real-world problems.