Data Scientist - AI Trading Agents (Web3)

Blockchain Academics; Singapore; Remote; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Remote-first work environment

Work-from-home arrangement

Job summary

Binance is seeking a talented AI/ML researcher to design and implement AI-powered trading agents using Web3 data. The role involves post-training of large language models and building evaluation datasets to drive practical financial AI applications.

You will work with senior engineers on system design and deployment, requiring strong communication and rapid learning in a fast-paced environment. A 6-month commitment is expected.

Qualifications

• Pursuing or holding a Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Financial Engineering, Automation, or a related field.
• Hands-on experience with post-training techniques such as SFT and RLHF/RLAIF.
• Familiar with PyTorch or TensorFlow and common toolkits like Hugging Face Transformers, DeepSpeed, vLLM.
• Excellent communication skills and a strong willingness to learn quickly.
• Able to commit at least 6 months, working 5 days a week.

Responsibilities

• Design and develop AI-powered financial trading agency skills based on Web3 data and integrate them into practical systems.
• Perform post-training of large-scale language models to enhance the model’s ability to utilize Binance’s internal trading skills.
• Assist in building and maintaining benchmark datasets to evaluate AI systems and iterate workflow prototypes for financial agent systems.
• Collaborate with senior engineers on system design, integration, and deployment.

Skills

SFT

RLHF

RLAIF

PyTorch

TensorFlow

Hugging Face

DeepSpeed

Open communication

Education

Bachelor’s/Master’s/PhD in CS, AI, Financial Engineering, Automation

Tools

Llama Factory

Slime

Verl

OpenRLHF

Hugging Face Transformers

DeepSpeed

vLLM

Job description

Binance is seeking a talented AI/ML researcher to design and implement AI-powered trading agents using Web3 data. The role involves post-training of large language models and building evaluation datasets to drive practical financial AI applications.

You will work with senior engineers on system design and deployment, requiring strong communication and rapid learning in a fast-paced environment. A 6-month commitment is expected.