NVIDIA is looking for an exceptional Engineering Manager to lead, scale, and innovate our core Data Labeling Platform. This is a highly visible, high-impact role where you will bridge the gap between bleeding-edge AI engineering, scalable software systems, and massive-scale operations.
In this role, you will lead a team of highly talented engineers to design and build next-generation data annotation platform. The team develops and manages software that supports a high volume of active annotation projects across diverse research areas, like Nemotron, Cosmos, Robotics and Red teaming delivering a large quantity of annotations through NVIDIA's internal data operations and external annotation partners. Your team’s focus will be driving operational efficiency through annotation interfaces, scalable backend workflows, models-in-the-loop, auto-labeling systems, data pipelines, and intelligent orchestration tools. We are looking for a leader who can thrive across a wide spectrum of experience. Whether you are a seasoned Engineering Manager looking to take on an expanded scope, an entry-level Manager looking to solidify your leadership footprint, or a Principal/Staff Engineer (IC) with deep architectural roots ready to transition into people management, we want to hear from you.
What you'll be doing:
System Design & Programming: Maintain a high technical bar. You will remain close to the code, guiding robust software design, ensuring clean data engineering practices, and occasionally jumping into hands-on Python programming when solving complex architectural bottlenecks.
People Leadership: Build, mentor, and lead a high-performing team of software, data, and AI application engineers. Foster a culture of technical excellence, accountability, and continuous growth.
Stakeholder Management: Serve as a critical bridge and strategic partner, aligning engineering roadmaps with high-level VPs, Research Leaders, and our Data Factory operations workforce.
AI Application Engineering: Architect and drive the implementation of next-generation auto-labeling applications that leverage multi-modal models-in-the-loop to dramatically reduce human labeling latency.
Data Engineering & Analytics: Own the data engineering layer that makes annotation work measurable: event logging, ETL into NVIDIA's data lake, the metrics, dashboards, and alerting built on it against defined reliability and latency targets
Front-End & Annotation Interfaces: Direct front-end engineering for custom annotation interfaces across text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate.
Operations Scaling: Optimize the platform for maximum scalability, data integrity, and throughput, ensuring the interface between human annotators and machine learning systems is seamless.
What we need to see:
Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field (or equivalent experience).
10+ overall years of professional software engineering experience, including 2+ years as a technical lead or engineering manager
Hands-on Engineering: Strong background as a Software Engineer, Data Engineer, or AI Application Engineer, with excellent system design skills and deep hands-on expertise in Python.
AI & Data Engineering: Proven experience in architecture-level understanding of data pipelines, distributed systems, and integrating machine learning models into production workflows (specifically auto-labeling or human-in-the-loop paradigms).
Leadership Capability: Experience leading technical initiatives, mentoring engineers, or formally managing a team. We welcome senior individual contributors (ICs) with demonstrated tech-lead experience who are ready to make the leap to formal management.
Exceptional Communication: Exceptional stakeholder management skills. You must be comfortable translating deeply technical constraints into strategic updates for VPs, while translating high-level business goals into operational directives for the Data Factory workforce.
Ways to stand out from the crowd:
Experience with human-in-the-loop data programs - managing data labeling platforms for RLHF or preference data or red teaming, supporting test, vision or multi-modal datasets.
Experience applying models to reduce or assist human effort in a labeling workflow, such as automated pre-labeling, model-based quality evaluation, or agent-driven internal tooling, with measurement to back it up.
Working proficiency in TypeScript or JavaScript, and experience delivering applications where interaction design materially affects user throughput and error rate.
Background in multimodal data: video, 3D and point cloud, speech, or document understanding.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field, or equivalent experience
- 10+ years of professional software engineering experience
- 2+ years as a technical lead or engineering manager
- Strong background as a Software Engineer, Data Engineer, or AI Application Engineer
- Excellent system design skills
- Deep hands-on expertise in Python
- Architecture-level understanding of data pipelines and distributed systems
- Experience integrating machine learning models into production workflows, particularly auto-labeling or human-in-the-loop systems
- Experience leading technical initiatives, mentoring engineers, or formally managing a team
- Exceptional stakeholder management and communication skills
- Experience with human-in-the-loop data programs, RLHF, preference data, red teaming, or multimodal datasets
- Experience applying models to automate or assist human labeling workflows
- Working proficiency in TypeScript or JavaScript
- Experience delivering applications where interaction design affects user throughput and error rate
- Experience with video, 3D, point cloud, speech, or document data
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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