Production Engineer, Applied Machine Learning Engine (Singapore)

ByteDance
Singapore
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: bachelorsSkills: ["Ownership","Analytical skills","Problem-solving","Cross-functional collaboration"]

Build and operate highly available machine learning training, inference, and storage systems for large-scale AML services. Own production operations stability, including scheduling/orchestration, Kubernetes and GPU cluster management, incident diagnosis, SLO/SLA governance, observability, on-call, and disaster recovery. Drive CI/CD with canary deployments, automated rollback, health checks, and capacity forecasting, while optimizing GPU/CPU/storage/network resource governance and performance.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
ByteDance
ByteDance
1 month ago

Production Engineer, Applied Machine Learning Engine (Singapore)

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 30 days agoStatus: Live
Reposted: similar role first listed 1 month ago

Job Summary

Build and operate highly available machine learning training, inference, and storage systems for large-scale AML services. Own production operations stability, including scheduling/orchestration, Kubernetes and GPU cluster management, incident diagnosis, SLO/SLA governance, observability, on-call, and disaster recovery. Drive CI/CD with canary deployments, automated rollback, health checks, and capacity forecasting, while optimizing GPU/CPU/storage/network resource governance and performance.
Location: Singapore
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Manage production operations and ensure stability for AML training, inference, and storage systems across scheduling/orchestration, Kubernetes/GPU clusters, distributed training, online inference serving, and Parameter Server/NoSQL storage.
  • •Build and maintain SLO/SLA frameworks, observability, alerting, on-call processes, incident diagnosis, self-healing mechanisms, disaster recovery, and post-incident reviews.
  • •Develop engineering capabilities with CI/CD, canary/gradual deployments, automated rollback, system health inspections, pre-flight checks, capacity forecasting, and elastic scaling.
  • •Lead resource governance and optimization across GPU, CPU, storage, and network infrastructure, including quota management, cost attribution, and performance tuning.
  • •Drive automated systems and pipelines to support highly available model serving and model training services.

Key Requirements

  • •Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
  • •Proficiency in Linux and at least one of Shell, Python, Go, or C++.
  • •Familiarity with ML training/inference architectures, Kubernetes, GPU clusters, or distributed storage systems.
  • •Experience troubleshooting production issues, analyzing performance, and developing automation platforms.
  • •Strong ownership, analytical/problem-solving skills, and ability to collaborate cross-functionally to resolve complex technical challenges.
Experience:Machine learning
Education:Bachelor's
Skills:OwnershipAnalytical skillsProblem-solvingCross-functional collaboration
Tech Stack:LinuxShellPythonGoC++KubernetesK8sGPU clustersDistributed trainingOnline inference servingParameter ServerNoSQLSLO/SLAObservabilityCI/CDCanary deploymentsAutomated rollbackDisaster recoveryPostmortemCapacity forecasting

Company Brief

ByteDance
Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.
Industry: Digital Media
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Headquarters: Beijing, China
Founded: 2012
WebsiteLinkedIn