NVIDIA

4 weeks ago

Senior MLOps Manager

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Lead and optimize NVIDIA's ground-truth data operations for autonomous driving, overseeing end-to-end GT production from requirements to delivery to model training. Manage internal teams and external vendors, define labeling specs, monitor throughput and quality metrics, and drive data-driven improvements, partnering with data engineering, ML, and tooling teams.

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Business Operations

Seniority: Sr. Manager level

Key Responsibilities

•Own the GT production and delivery pipeline, ensuring datasets are delivered on time, at the right quality, and at the right scale for model training and validation.
•Define GT requirements with Data Analyst and MLE teams, translating them into labeling specs, QA specs, volumes, SLAs, and quality targets.
•Manage day-to-day GT operations across internal teams and external vendors, including task allocation, throughput tracking, and issue triage.
•Establish and monitor key operational metrics (throughput, quality, rework rate, audit pass rate, labeling cost), driving continuous improvement using data-driven insights.
•Partner with tooling and data infrastructure teams to improve labeling tools, QA workflows, and GT data pipelines, including opportunities to leverage AI agents to boost efficiency.

Key Requirements

•BS degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
•10+ years of overall professional experience in data operations, labeling operations, ML data programs, or related fields, working with large-scale data or ML-driven products.
•5+ years of direct leadership/management experience leading data operations, labeling teams, or cross-functional GT programs with KPIs and SLAs.
•Proven track record owning end-to-end data or GT operations, with examples of improving quality, efficiency, and reliability.
•Strong communication and stakeholder management skills, with experience across engineering, ML, product, and vendor teams.

Experience:10+ yearsAutonomous drivingMl dataAi data ops

Education:Bachelor's

Skills:LeadershipCommunicationStakeholder managementProblem-solvingData-driven decision making

Tech Stack:Annotation toolsDataset managementAi agentsQa workflowsGt pipelinesModel training

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

4 weeks ago

Senior MLOps Manager

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Business Operations

Seniority: Sr. Manager level

Key Responsibilities

•Own the GT production and delivery pipeline, ensuring datasets are delivered on time, at the right quality, and at the right scale for model training and validation.
•Define GT requirements with Data Analyst and MLE teams, translating them into labeling specs, QA specs, volumes, SLAs, and quality targets.
•Manage day-to-day GT operations across internal teams and external vendors, including task allocation, throughput tracking, and issue triage.
•Establish and monitor key operational metrics (throughput, quality, rework rate, audit pass rate, labeling cost), driving continuous improvement using data-driven insights.
•Partner with tooling and data infrastructure teams to improve labeling tools, QA workflows, and GT data pipelines, including opportunities to leverage AI agents to boost efficiency.

Key Requirements

•BS degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
•10+ years of overall professional experience in data operations, labeling operations, ML data programs, or related fields, working with large-scale data or ML-driven products.
•5+ years of direct leadership/management experience leading data operations, labeling teams, or cross-functional GT programs with KPIs and SLAs.
•Proven track record owning end-to-end data or GT operations, with examples of improving quality, efficiency, and reliability.
•Strong communication and stakeholder management skills, with experience across engineering, ML, product, and vendor teams.

Experience:10+ yearsAutonomous drivingMl dataAi data ops

Education:Bachelor's

Skills:LeadershipCommunicationStakeholder managementProblem-solvingData-driven decision making

Tech Stack:Annotation toolsDataset managementAi agentsQa workflowsGt pipelinesModel training

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor