Large Language Model Training System Engineer Graduate (Applied Machine Learning) - 2027 Start

Volcano Ark is an all-in-one large model service platform launched by Volcano Engine. It is a leading platform in China's large model market by product capability and market share. The platform provides end-to-end services including model inference, evaluation, fine-tuning, AI application development, and a plugin ecosystem. Volcano Ark hosts Doubao and leading industry large models, and supports enterprise AI adoption through stable, secure, and trusted solutions as well as professional algorithm and technical services. Team Introduction:

Data AML is ByteDance's machine learning platform team. It provides training and inference systems for recommendation, advertising, computer vision, speech, and NLP scenarios across products such as Douyin, Toutiao, and Xigua Video. The team also supports internal business teams with large-scale machine learning compute, explores general and innovative algorithms for business problems, and offers core machine learning and recommendation system capabilities to external enterprise customers through Volcano Engine.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities:

• Participate in the development of the Volcano Ark training system, enabling internal and external users to perform large model post-training, including SFT and RL, on the Volcano Ark platform in a serverless manner.
• Design elastic training solutions for complex multi-tenant training scenarios, supporting mixed multi-tenant training across multiple data centers and heterogeneous hardware, while improving training throughput and stability.
• Design reinforcement learning systems to improve training efficiency and provide user-friendly reinforcement learning training interfaces.Minimum Qualifications:
• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science or a related discipline.
• Proficient in one or more programming languages such as Python, Rust, or C++; writes clean code and has strong framework design and abstraction skills.
• Experience in training framework design or training system optimization; participation in large model training engineering or complex distributed system development is preferred.
• Strong interest in tracking and solving technical problems, studying low-level principles and performance bottlenecks, and applying scientific modeling methods.

Preferred Qualifications:

• Solid experience in computer architecture optimization, with familiarity with heterogeneous hardware and high-performance networking.
• Strong scientific optimization skills, including the ability to independently analyze training efficiency and identify optimization directions.
• Familiarity with mainstream reinforcement learning frameworks and technical insight into training frameworks.