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Backend Engineer - Applied Machine Learning Engine (Efficiency Tool)
Backend Engineer - Applied Machine Learning Engine (Efficiency Tool)
The Backend Engineer on the Applied Machine Learning (AML) team will research, design, and develop computer and network software and solutions for massively distributed recommendation systems. This role involves analyzing user needs, updating software capabilities, developing testing procedures, and integrating hardware with software systems.
About the role
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. On 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. DevOps ensures that the very centric machine learning services at TikTok have the highest level of availability, as well as creating highly automated systems and pipelines.
Responsibilities
- 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.
- Work with computer hardware engineers to integrate hardware and software systems and develop specifications and performance requirements.
Minimum Qualifications
- Bachelor’s degree in Computer Science or equivalent with 3+ 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.
- Expertise in DevOps technologies like Ansible, Terraform, Salt, Bash Scripting, etc.
- Experience in building solutions with AWS, GCP, Azures, AliCloud or other cloud services.
- Familiar with Unix/Linux operating systems.
Preferred Qualifications
- Ability to thrive in a fast-paced environment.
- Strong understanding of code optimizing and routine tasks automation.
- Proficiency in at least one machine learning framework: TensorFlow, PyTorch, MXNet or PaddlePaddle.
- Solid background of algorithms and data structures.