Job Description
Join ByteDanceâs Recommendation Architecture Team in Singapore, where you will play a pivotal role in shaping the next generation of recommendation platforms that power content discovery across TikTok, Toutiao, and other ByteDance products. As a Platform R&D Engineer, you will collaborate with worldâclass machine learning researchers, software engineers, and data scientists to design, build, and optimize highly scalable, lowâlatency systems that serve billions of recommendations daily.
You will be responsible for architecting robust microservices, enhancing data pipelines, and ensuring the reliability and performance of the recommendation stack. This role offers the opportunity to work on cuttingâedge technologies in distributed computing, realâtime streaming, and largeâscale machine learning, while driving innovation that impacts hundreds of millions of users worldwide.
If you are passionate about building highâperformance infrastructure, thrive in a fastâpaced environment, and want to see your work directly influence global user experiences, we encourage you to apply.
Responsibilities
- Design and develop scalable backend services and APIs for recommendation systems.
- Optimize data processing pipelines using stream processing frameworks (e.g., Apache Flink, Kafka).
- Collaborate with ML engineers to integrate recommendation models into production services.
- Ensure system reliability, latency, and throughput through performance tuning and monitoring.
- Drive adoption of best practices in code quality, testing, and CI/CD pipelines.
- Participate in architecture reviews and contribute to longâterm technical roadmap.
- Mentor junior engineers and foster a culture of continuous learning.
Qualifications
- Bachelorâs or Masterâs degree in Computer Science, Software Engineering, or a related field.
- 3+ years of experience building largeâscale distributed systems or backend platforms.
- Strong proficiency in languages such as Java, C++, Go, or Python.
- Experience with microservices architecture, RESTful/gRPC APIs, and service mesh.
- Familiarity with stream processing technologies (Kafka, Flink, Spark Streaming) and messaging queues.
- Understanding of data storage solutions including SQL, NoSQL, Redis, and distributed file systems.
- Knowledge of machine learning fundamentals and recommendation algorithms is a plus.
- Excellent problemâsolving skills, strong communication, and ability to work in a collaborative, multicultural team.