Machine Learning Engineer - Trae USDS

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. U.S. Data Security (“USDS”) is a subsidiary of TikTok in the U.S.

TRAE (The Real AI Engineer) is an intelligent engineering product capable of understanding requirements, orchestrating tools, and independently completing development tasks, providing users with end-to-end software generation capabilities. As one of the most popular AI programming products and the world’s first end-to-end AI software development agent, TRAE covers a full spectrum of development scenarios, from simple to highly complex. We are looking for passionate and creative engineers to join us in reshaping the development paradigm and defining the future of AI-driven software engineering.

We are seeking a Machine Learning Engineer to join the core R&D team of TRAE, responsible for model training, optimization, quantization, and deployment. You will work closely with top engineers and researchers to explore and implement LLM training and deployment for software engineering, driving the continuous evolution of TRAE's intelligent engineering capabilities.

Responsibilities
- Design, train, and fine-tune large language models (LLMs) that support TRAE’s core reasoning and code generation capabilities.
- Build efficient, stable, and scalable model training and evaluation pipelines.
- Collaborate with infrastructure and product teams to deploy and monitor models efficiently on GPU clusters.
- Continuously optimize models for latency, throughput, and accuracy.
- Stay up to date with and apply cutting-edge techniques in large model optimization and inference acceleration.

Minimum Qualifications
- Bachelor’s degree or above in Computer Science, Electrical Engineering, Mathematics, or a related field.
- Solid foundation in machine learning, deep learning, and optimization algorithms.
- Proficiency in PyTorch or TensorFlow, with programming skills in Python and C++/CUDA.
- Experience with large-scale distributed training, mixed-precision training, or model parallelism.
- Hands-on experience with model quantization, pruning, or CUDA-based deployment optimization.
- Passionate about building efficient, production-ready AI systems and advancing the future of AI-driven software development.

Preferred Qualifications
- Master’s degree or above in Computer Science, Electrical Engineering, Mathematics, or a related field.
- 3+ years of experience in tech.