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 systems around the world.
The efficiency tools team provides a high-quality tool platform and technical outputs for the efficiency and training of online models and the management of the team's massive hardware resources.
Responsibilities:
- Research, design, and develop computer and network software or specialised utility programs.
- Analyze user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis.
- Update software, enhances existing software capabilities, and develops and direct software testing and validation procedures.
- Work with computer hardware engineers to integrate hardware and software systems and develop specifications and performance requirements.
- Research, design, and develop computer and network software or specialised utility programs.
- Analyse user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis.
- Update software, enhances existing software capabilities, and develops and direct software testing and validation procedures.
Minimum Qualifications:
- Bachelor’s degree in Computer Science or equivalent with 1+ years of relevant experience
- Proven experience in analyzing and troubleshooting distributed systems.
- Prior experience designing and maintaining large-scale systems.
- Experience programming in at least one of the following languages: Python or Golang.
- Familiar with components, such as Mysql, Redis, Kafka, Clickhouse, Terraform and K8S, etc.
- Expertise in DevOps technologies like Monitor, Alarm, Tracing, CMDB etc.
- Experience in building solutions with AWS, Azures, AliCloud or other cloud services.
Preferred Qualifications:
- Familiar with Unix/Linux operating systems.
- Ability to thrive in a fast-paced environment.
- Strong understanding of code optimizing and routine tasks automation.
- Extra points will be given for proficiency in at least one machine learning framework: TensorFlow, PyTorch, MXNet or PaddlePaddle.
- Solid background of algorithms and data structures.
