Machine Learning Engineer, TikTok Machine Learning Infrastructure
3 weeks ago
About Bytedance
Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Helo, and Resso, as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.
Why Join Us
Creation is the core of ByteDance's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible.
Together, we inspire creativity and enrich life - a mission we aim towards achieving every day.
To us, every challenge, no matter how ambiguous, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At ByteDance, we create together and grow together. That's how we drive impact - for ourselves, our company, and the users we serve.
Join us.
About the Team
The mission of our AML team is to push next-generation machine learning algorithms and platform for the recommendation system, ads ranking and search ranking in our company. We also drive substantial impact for core businesses of the company. Currently we are looking for Software Engineer - Machine Learning Infrastructure to join our team to support and advance that mission.
Responsibilities
- Responsible for the design and implementation of a global-scale machine learning system for feeds, ads and search ranking models.
- Responsible for improving use-ability and flexibility of the machine learning infrastructure.
- Responsible for improving the workflow of model training and serving, data pipelines and resource management for the multi-tenancy machine learning systems
- Responsible for designing and developing key components of ML infrastructure and mentoring interns
- Support the production of scalable and optimised AI/machine learning (ML) models
- Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models
- Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process
- Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm
- Work with the relevant software platforms in which the models are deployed
Qualifications
- B. Sc or higher degree in Computer Science or related fields from accredited and reputable institutions.
- Proficient in C/C++/Python, and have solid programming skills.
- Familiar with deep learning frameworks (TensorFlow/Pytorch).
- Experience in developing and deploying large-scale systems.
- Ability to work independently and complete projects from beginning to end and in a timely manner.
- Good communication and teamwork skills to clearly communicate technical concepts with other teammates.
- Experience on improving core machine learning infrastructure(TensorFlow, Pytorch, and Jax).
Preferred Qualifications:
- Experience contributing to an open sourced machine learning framework (TensorFlow/PyTorch).
- Experience in big data frameworks (e.g., Spark/Hadoop/Flink), experience in resource management and task scheduling for large scale distributed systems.
- Strong background in one of the following fields: Hardware-Software Co-Design, High Performance Computing, ML Hardware Acceleration (e.g., GPU/TPU/RDMA) or ML for Systems.
Bytedance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At Bytedance, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
Tell employers what skills you have
Mentoring
Machine Learning
Big Data
Hardware
Pipelines
Scripting
PyTorch
Distributed Systems
High Performance Computing
AML
Statistics
Data Science
Resource Management
Scheduling
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