Backend Software Engineer, TikTok Live Recommendation Infrastructure

TikTok
San Jose
Workplace: OnsiteFull timeFunction: Software EngineeringExperience: 3+ yearsEducation: bachelorsSkills: ["Problem-solving","Collaboration","Performance optimization","Cost-efficiency","System robustness"]

Build and optimize backend infrastructure for TikTok Live recommendation workloads, covering training, inference, and data pipelines. Develop distributed training pipelines and low-latency inference serving, and architect data pipelines for offline feature engineering and ranking models. Partner with applied ML engineers and researchers to productionize models and integrate them into the Live recommendation stack, while driving performance improvements, scalability, robustness, and cost efficiency in high-traffic streaming scenarios.

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FursaFursa
TikTok
TikTok
1 month ago

Backend Software Engineer, TikTok Live Recommendation Infrastructure

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 19 hours agoStatus: Live

Job Summary

Build and optimize backend infrastructure for TikTok Live recommendation workloads, covering training, inference, and data pipelines. Develop distributed training pipelines and low-latency inference serving, and architect data pipelines for offline feature engineering and ranking models. Partner with applied ML engineers and researchers to productionize models and integrate them into the Live recommendation stack, while driving performance improvements, scalability, robustness, and cost efficiency in high-traffic streaming scenarios.
Location: San Jose
Workplace: Onsite
Employment Type: Full time
Job Function: Software Engineering
Seniority: Mid level

Key Responsibilities

  • •Design and build backend systems for large-scale recommendation workloads, including training, inference, and data pipelines.
  • •Develop robust model infrastructure with distributed training pipelines and low-latency inference serving.
  • •Architect and improve data pipelines for efficient collection, preprocessing, and offline feature engineering for recommendation and ranking models.
  • •Collaborate with ML engineers and researchers to productionize models and integrate them into the TikTok Live recommendation stack.
  • •Drive performance optimization and cost-efficiency across training, inference, and data workflows while ensuring scalability and maintainability.

Key Requirements

  • •Bachelor's degree or above in Computer Science, Engineering, or a related technical field.
  • •At least 3 years of experience in strong programming skills in C++, Go, or Java, plus scripting experience in Python.
  • •Solid experience in distributed systems and backend service development.
  • •Hands-on experience with ML infrastructure such as model serving, inference optimization, or large-scale training systems.
  • •Proficiency building and maintaining data pipelines using Spark, Flink, Kafka, Hadoop, or similar.
Experience:3+ yearsMachine learning infrastructureDistributed systemsBackend servicesRecommendation systems
Education:Bachelor's in Computer Science, Engineering
Skills:Problem-solvingCollaborationPerformance optimizationCost-efficiencySystem robustness
Tech Stack:C++GoJavaPythonSparkFlinkKafkaHadoopTensorFlowPyTorchKubernetesContainer orchestration

Company Brief

TikTok
Short-form video platform that lets users create, share, and discover entertainment content through algorithmic recommendations. It also offers advertising and creator tools for brands, influencers, and businesses.
Industry: Digital Media
Company Size: Enterprise (1,001+ employees)
Growth: Scaleup
Headquarters: Singapore, Singapore
Founded: 2016
WebsiteLinkedIn