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Assistant Ai Labeling Jobs in California (NOW HIRING)

TSMG

AI/ML Data Contributor

Los Angeles, CA · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

TSMG

AI/ML Data Contributor

Los Angeles, CA

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

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Assistant Ai Labeling information

What is an assistant AI labeling?

Assistant AI Labeling jobs involve reviewing, tagging, and categorizing data such as images, text, or audio to help train artificial intelligence and machine learning models. These roles are essential because accurate labeling ensures that AI systems can learn to recognize patterns and make decisions effectively. Tasks may include drawing bounding boxes around objects in images, transcribing spoken words, or classifying text according to given guidelines. The work is often done using specialized software and requires attention to detail as well as consistency. Assistant AI Labelers may work remotely or in-house for tech companies, research organizations, or data annotation firms.

What are the key skills and qualifications needed to thrive as an assistant AI labeling?

To thrive as an Assistant AI Labeling Specialist, you need strong attention to detail, accuracy, and an understanding of data annotation practices, often supported by a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools, and sometimes basic knowledge of programming languages like Python is beneficial. Dependability, time management, and the ability to follow complex instructions are crucial soft skills for excelling in this role. These skills ensure high-quality labeled data, which is vital for training accurate and reliable AI models.

What are some common challenges faced by assistant AI labeling professionals, and how can they be addressed?

Assistant AI Labeling professionals often encounter challenges such as maintaining consistency and accuracy when labeling large volumes of data, especially with ambiguous or subjective cases. To address these challenges, most teams implement clear annotation guidelines, regular training sessions, and peer review processes to ensure high-quality outputs. Collaboration with data scientists and project managers is also key, as open communication helps clarify uncertainties and align labeling practices with project goals. Embracing feedback and staying flexible as guidelines evolve can further enhance both the quality of work and job satisfaction in this role.

What is the difference between Assistant Ai Labeling vs Data Annotator?

AspectAssistant Ai LabelingData Annotator
CredentialsHigh school diploma or equivalent; some roles may require basic technical skillsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentRemote or office-based; collaborative with AI teamsPrimarily remote; focused on data labeling tasks
Industry UsageAI development, machine learning projectsData preparation for AI, machine learning, and analytics
Search & Comparison IntentUnderstanding roles supporting AI trainingData labeling and annotation tasks for AI models

Assistant Ai Labeling involves supporting AI systems by labeling data, often requiring some technical understanding. Data Annotator focuses on labeling data to prepare datasets for AI training. Both roles are essential in AI development, with overlapping skills but different focus areas.

What are the most commonly searched types of Ai Labeling jobs in California?

The most popular types of Ai Labeling jobs in California are:

What are popular job titles related to Assistant Ai Labeling jobs in California?

For Assistant Ai Labeling jobs in California, the most frequently searched job titles are:

What job categories do people searching Assistant Ai Labeling jobs in California look for?

The top searched job categories for Assistant Ai Labeling jobs in California are:

What cities in California are hiring for Assistant Ai Labeling jobs?

Cities in California with the most Assistant Ai Labeling job openings:

Assistant Ai Labeling jobs near you

Engineering Manager, Data Labeling Platform

Santa Clara, CA • On-site

Nvidia Corporation
Computer and Electronic Product Manufacturing • 10K+ employees

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

7th of 246 rated software companies

Great coworkers

People enjoy working here

Good employer

Learn more about Nvidia Corporationexternal

Full-time

Posted 5 days ago

Job description

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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 200,000 USD - 322,000 USD for Level 3, and 248,000 USD - 391,000 USD for Level 4.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 28, 2026.
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.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

View All Nvidia Jobs

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