ByteDance

2 days ago

Senior Production Engineer - Applied Machine Learning

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 35 minutes agoStatus: Live

Job Summary

Own production management and stability for AML (Applied Machine Learning) training, inference, and storage systems. Build reliability capabilities including SLO/SLA, observability, alerting, on-call operations, incident post-mortems, auto-healing, and disaster recovery. Drive engineering excellence with CI/CD, canary releases, auto-rollback, automated inspections, and capacity forecasting with elastic scaling. Manage GPU/CPU/storage/network governance to improve availability and resource efficiency across core ML pipelines.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Own production management and stability for AML training, inference, and storage systems, covering scheduling/orchestration, K8s/GPU clusters, distributed training, online inference serving, and ParameterServer/NoSQL storage.
•Build and maintain reliability mechanisms including SLO/SLA, observability, alerting, on-call processes, fault diagnosis, auto-healing, disaster recovery, and incident post-mortems.
•Drive engineering excellence via CI/CD, canary releases, auto-rollback, automated inspections, pre-flight checks, capacity forecasting, and elastic auto-scaling.
•Oversee resource governance across GPU/CPU/storage/network, including quota management, cost attribution, and performance tuning to improve availability and utilization.
•Collaborate cross-functionally to troubleshoot and resolve complex production issues and improve R&D efficiency.

Key Requirements

•Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
•Familiar with Linux and proficient in at least one of Shell, Python, Go, or C++.
•Understanding of ML training/inference architectures, Kubernetes, GPU clusters, or distributed storage systems.
•Proven experience in online troubleshooting, performance analysis, and building automation platforms.
•Strong sense of responsibility and the ability to drive resolution of complex issues across cross-functional teams.

Experience:Machine learningDistributed systems

Education:Bachelor's

Skills:ResponsibilityLogical thinkingCross-functional collaborationProblem-solving

Tech Stack:LinuxShellPythonGoC++KubernetesGPU clustersDistributed trainingOnline inference servingParameterServerNoSQLCI/CDCanary releasesAuto-rollbackObservabilityAlertingAuto-healingDisaster recoveryCapacity forecastingElastic auto-scaling

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

Website LinkedIn

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ByteDance

2 days ago

Senior Production Engineer - Applied Machine Learning

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 35 minutes agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Own production management and stability for AML training, inference, and storage systems, covering scheduling/orchestration, K8s/GPU clusters, distributed training, online inference serving, and ParameterServer/NoSQL storage.
•Build and maintain reliability mechanisms including SLO/SLA, observability, alerting, on-call processes, fault diagnosis, auto-healing, disaster recovery, and incident post-mortems.
•Drive engineering excellence via CI/CD, canary releases, auto-rollback, automated inspections, pre-flight checks, capacity forecasting, and elastic auto-scaling.
•Oversee resource governance across GPU/CPU/storage/network, including quota management, cost attribution, and performance tuning to improve availability and utilization.
•Collaborate cross-functionally to troubleshoot and resolve complex production issues and improve R&D efficiency.

Key Requirements

•Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
•Familiar with Linux and proficient in at least one of Shell, Python, Go, or C++.
•Understanding of ML training/inference architectures, Kubernetes, GPU clusters, or distributed storage systems.
•Proven experience in online troubleshooting, performance analysis, and building automation platforms.
•Strong sense of responsibility and the ability to drive resolution of complex issues across cross-functional teams.

Experience:Machine learningDistributed systems

Education:Bachelor's

Skills:ResponsibilityLogical thinkingCross-functional collaborationProblem-solving

Company Brief

ByteDance

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn