Team Introduction
Data AML is ByteDance’s machine learning platform, providing recommendation/advertising/CV (Computer Vision)/speech/NLP (Natural Language Processing) training and inference systems for Bytedance businesses like Tiktok and Douyin. It empowers business partners with enormous ML computing power and ground-breaking algorithmic innovation. Additionally, it provides core capabilities of machine learning/recommendation systems to external enterprise customers via Volcano Engine/BytePlus.
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 hte 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
-Distributed Orchestration & Scheduling by providing support on distributed deployment of services/model training and inference tasks)
-Optimize allocation rate and resource operation efficiency globally and coordinate capacity of various heterogeneous resources (CPU/GPU/heterogeneous hardware/training data)
-Participate in development of training-related requirements (incremental training and API for online learning) and enhance framework efficiency
-Participate in offline-to-online synchronization, data consistency, and model-update timeliness optimization and handle traffic scheduling for heterogeneous resources
-Individuals who are completing or have recently completed a Bachelor's degree in computing or a related discipline.
-Proficient in at least one programming language (Go/Python preferred) in Linux; good programming habits and coding skills
-Familiar with distributed system principles and open-source distributed scheduling frameworks
-Strong sense of responsibility; good learning, communication skills, and self-motivated; quick to respond and act.
-Experience in machine learning system practice
-Experience in open-source ML orchestration frameworks (e.g. Ray/TFX/Kubeflow)
