Machine Learning Engineer - Orchestration

ByteDance
San Jose
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: bachelorsSkills: ["Logical analysis","Communication","Self-motivation","Documentation"]

Build and optimize distributed orchestration and scheduling for large-scale machine learning training and online inference, improving cluster efficiency and resource utilization across Kubernetes/Godel and multi-region/multi-cloud scenarios. Develop next-generation training and online inference architectures for ultra-large-scale recommendation models, extend autoscaling and parallelization, and integrate with platform tooling to improve diagnostics and usability. Partner on research-driven model paradigms and MLops workflows.

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FursaFursa
ByteDance
ByteDance
1 month ago

Machine Learning Engineer - Orchestration

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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 optimize distributed orchestration and scheduling for large-scale machine learning training and online inference, improving cluster efficiency and resource utilization across Kubernetes/Godel and multi-region/multi-cloud scenarios. Develop next-generation training and online inference architectures for ultra-large-scale recommendation models, extend autoscaling and parallelization, and integrate with platform tooling to improve diagnostics and usability. Partner on research-driven model paradigms and MLops workflows.
Location: San Jose
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Optimize resource efficiency in distributed orchestration and scheduling by developing/using scheduling frameworks around the Kubernetes/Godel ecosystem and improving cluster utilization strategies.
  • •Extend autoscaling and parallelization for models and business operations using load modeling to optimize resource requests and utilization at scale.
  • •Implement service preemption/eviction and handle borrowing/mixed deployment across resources, clusters, and scenarios spanning multiple data centers, regions, and clouds.
  • •Build distributed training architecture for next-generation ultra-large recommendation models, including robust training runtime, GPU synchronization, and distributed computing APIs for research paradigms.
  • •Construct online orchestration architecture for next-generation recommender systems by optimizing distributed inference around online training scenarios and integrating research/experimental models with MLops.

Key Requirements

  • •Bachelor’s degree or above in Computer Science or a similar field.
  • •At least 5 years of experience with strong hands-on coding in Go or Python in a Linux environment.
  • •Familiar with open-source distributed scheduling frameworks such as Kubernetes (K8S), Yarn, Flink, MapReduce, Mesos, and Celery, with experience building machine learning systems.
  • •Strong understanding of distributed systems and experience designing, developing, and maintaining large-scale distributed systems.
  • •Excellent logical analysis and ability to abstract and split business logic, with good documentation habits.
Experience:Machine learningDistributed systemsRecommendation
Education:Bachelor's
Skills:Logical analysisCommunicationSelf-motivationDocumentation
Tech Stack:GoPythonLinuxKubernetesGodelYarnFlinkMapReduceHadoopMesosCeleryPyTorchTensorFlowK8SRLFinetuneDistillationGPUAutoScaling

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