Backend Applied Machine Learning Engineer Graduate (AML Efficiency Tool) - 2027 Start
Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Jul 17, 2026.
岗位职责 / Responsibilities
Team Introduction
The Applied Machine Learning (AML) team combines system engineering and the art of machine learning to develop and run massively distributed recommendation system around the world. In the team, you'll have the opportunity to sharpen your expertise in coding, performance analysis and large system operation, and get heavily involved in the process of hardware/capacity decision-making. You ensure that the very centric machine learning services at ByteDance have the highest level of availability, as well as creating highly automated systems and pipelines.
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance.
Successful candidates must be able to commit to an onboarding date by end of year 2027. 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 ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
About The Team
The Applied Machine Learning (AML) team combines system engineering and the art of machine learning to develop and run massively distributed recommendation system around the world.
In the team, you'll have the opportunity to sharpen your expertise in coding, performance analysis and large system operation, and get heavily involved in the process of capacity decision-making. You ensure that the very centric machine learning services at Bytedance have the highest level of availability, as well as creating highly automated systems and pipelines.
Responsibilities
- Responsible for building and evolving Lagrange - the central console platform that supportsAl-related engineering workflows across the organization
- Analyse user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis.
- Work on problems with real technical depth and long-term impact: platform architecture, engineering productivity, system scalability, operational intelligence, and the practical integration of Al into engineering and operations workflows.
- Create the infrastructure and platform capabilities that make large-scale Al development more efficient, reliable, and intelligent
任职要求 / Requirements
Minimum Qualifications
- Individuals who are completing or have recently completed a Bachelor's/ Master's degree in computing or a related discipline.
- Proficient in at least one of Python/Golang/C++, with solid coding skills and good coding practices.
- Proficient in using common storage and middleware systems such as MySQL, Redis, and MQ, with basic troubleshooting and performance tuning capabilities.
- Familiar with Python frameworks and libraries such as Flask, Celery, Django, Tornado, NumPy; or familiar with common Go open-source frameworks such as Beego, Gin, Gorm, Sarama, and gRPC-Go.
- Understand and have experience with common time-series/data components such as OpenTSDB, Prometheus, InfluxDB, and OLAP databases like ClickHouse.
- Strong sense of responsibility, good learning ability, communication skills, and self-motivation, with solid team collaboration.
- Good documentation habits, able to write and update workflow and technical documents in a timely manner as required.
Preferred Qualifications
- Experience in upper-layer business systems such as search systems or recommendation systems (development or operations) is a plus.
- Experience with the React framework and its ecosystem; familiarity with visualization stacks such as ECharts, AntV, and D3.js is preferred.
- Hands-on experience with AI Agent development is preferred.
- Familiarity with cloud computing concepts, including virtual machines and containers; a solid understanding of networking and message queues is a plus.
- Experience building CPU/GPU resource management platforms; familiarity with common NVIDIA GPU architectures; and experience with anomaly diagnosis and troubleshooting is preferred.
- Experience with distributed storage systems such as HDFS and key-value stores like LevelDB/RocksDB is preferred.