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Flexible Remote Image Annotation Jobs in California

Senior AI Engineer

Los Altos, CA · On-site +1

$123K - $169K/yr

We offer a flexible, remote working environment. You can expect a warm welcome from a friendly and ... Experience with multi-modal AI (voice, image, video generation or processing) * Familiarity with ...

Senior AI Engineer

Los Altos, CA · On-site +1

$123K - $169K/yr

We offer a flexible, remote working environment. You can expect a warm welcome from a friendly and ... Experience with multi-modal AI (voice, image, video generation or processing) * Familiarity with ...

Viral genome reconstruction, annotation, consensus generation, lineage or subtype assignment, and ... Genuinely flexible schedules - we just ask that you communicate when you're coming in later than ...

Senior Radar Systems Engineer

Santa Barbara, CA · On-site +1

$116K - $159K/yr

Remote Sensing (the data), Space Systems (the components), and Mission Solutions (the platforms ... Experience in writing algorithms specifically for SAR image processing. * Bachelor's degree or ...

Staff Optical Engineer

Mountain View, CA · On-site +1

$166K - $223K/yr

... CA or Remote, developing infrared remote sensing instruments. The ideal candidate is a self ... Own and manage comprehensive optical error budgets and performance metrics, including image quality ...

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Flexible Remote Image Annotation information

What is flexible remote image annotation?

Flexible remote image annotation is a job where individuals label or tag elements within digital images from a remote location, often from home. This work is crucial for training artificial intelligence and machine learning models, particularly in fields like computer vision and autonomous vehicles. The 'flexible' aspect means workers can often set their own hours and choose tasks according to their availability. Image annotation tasks may include outlining objects, assigning categories, or describing visual content in images. Most positions require attention to detail and basic computer skills, but prior experience is not always necessary.

What are the key skills and qualifications needed to thrive as a flexible remote image annotation specialist?

To thrive as a Flexible Remote Image Annotation Specialist, you need strong attention to detail, visual accuracy, and a basic understanding of image processing, often supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox, CVAT, or VIA, and sometimes experience with basic data entry platforms, is typically required. Excellent time management, communication skills, and the ability to work independently are valued soft skills for this remote role. These skills ensure high-quality, consistent data labeling essential for training reliable machine learning models and supporting AI development.

What are some common challenges faced in flexible remote image annotation roles and how can they be managed?

One common challenge in flexible remote image annotation is maintaining accuracy and consistency across large datasets, especially when guidelines are complex or images are ambiguous. Working independently can also make it harder to get immediate feedback or clarification. To manage these challenges, it’s important to regularly review annotation guidelines, participate in team check-ins or forums, and make use of quality assurance tools provided by the employer. Staying organized and communicating proactively with project leads can help ensure your work meets expectations and deadlines.

What is the difference between Flexible Remote Image Annotation vs Data Labeler?

AspectFlexible Remote Image AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageAI, machine learning, computer visionAI, machine learning, data processing
Job FocusAnnotating images with labels, bounding boxes, segmentationLabeling data, categorizing images or text

Flexible Remote Image Annotation and Data Labeler roles both involve data processing tasks in AI and machine learning industries. While image annotation focuses on marking specific features within images, data labelers may work with various data types, including text and images. Both roles are remote, require similar skills, and serve the same industry needs, but image annotation emphasizes visual data precision.

What are the most commonly searched types of Remote Image Annotation jobs in California?

The most popular types of Remote Image Annotation jobs in California are:

What are popular job titles related to Flexible Remote Image Annotation jobs in California?

For Flexible Remote Image Annotation jobs in California, the most frequently searched job titles are:

What job categories do people searching Flexible Remote Image Annotation jobs in California look for?

The top searched job categories for Flexible Remote Image Annotation jobs in California are:

What cities in California are hiring for Flexible Remote Image Annotation jobs?

Cities in California with the most Flexible Remote Image Annotation job openings:

Senior Technical Program Manager, Human-in-the-Loop Operations

Roblox

San Mateo, CA • On-site, Remote

Full-time

Posted 6 days ago


Job description

As Senior Technical Program Manager for the Human-in-the-Loop (HITL) Operations, you will own the end-to-end lifecycle of Roblox's AI data labeling and model evaluation ecosystem. This is a high-leverage, high-ownership role at the intersection of Research, Engineering, and Operations. You will scale our data operations that is handling million of items evaluated annually today with projection of 4x the current volume by 2030, managing a multi-million annual budget and a distributed workforce of 100+ remote contractors - all while driving the platform evolution from manual workflows to AI-assisted, LLM-accelerated operations.

This is a rare opportunity to build the data infrastructure that directly determines the quality of Roblox's AI models across creator tools, content discovery, in-experience AI, and 3D generative content - at the exact moment when human judgment is the critical ingredient for getting these models right.

You Will

  • Lead data programs end-to-end. Own the full lifecycle of labeling and model evaluation workflows across Roblox's AI teams - from translating ambiguous ML requirements into structured annotation tasks, to overseeing contractor execution, quality review, and dataset delivery to model teams.
  • Define and maintain ground truth. Develop and iterate on labeling guidelines, annotation schemas, and quality frameworks tailored to Roblox's unique data types: 3D mesh quality, texture coherence, search relevance, game novelty detection, NPC behavior evaluation, and AI-generated Luau code assessment.
  • Manage a multi-vendor, distributed workforce. Oversee remote contractor teams, managing workforce allocation, productivity SLAs, quality calibration, and budget forecasting serving across multiple teams with Roblox.
  • Drive the shift to AI-assisted workflows. Partner with Engineering to evaluate and implement LLM-based pre-labeling, LLM-as-a-Judge evaluation, and automated QA routing - reducing FTE coordination overhead and scaling throughput proportional to our annual volume growth.
  • Partner cross-functionally across Roblox AI. Work closely with ML Engineers, Data Scientists, and Product Managers to translate model development needs into concrete, executable data programs, and communicate program status and quality trends to senior leadership.
  • Own operational excellence and KPIs. Define and track SLAs across throughput, inter-annotator agreement, cost per label, and data quality metrics. Surface risks, resolve bottlenecks, and drive continuous process improvement.
  • Shape platform and tooling strategy. Contribute to Roblox's next-generation labeling and evaluation platform strategy - including vendor pilots, internal platform improvements, and the transition to a hybrid human-AI collaborative workflow model.

You Have

  • 5+ years of experience in Technical Program Management, Data Operations, or Operations Management within an AI/ML or data-intensive environment.
  • Deep hands-on experience with data labeling, annotation, or model evaluation - including designing annotation schemas, writing labeling guidelines, and managing quality control at scale.
  • Proven experience managing external vendor relationships and distributed contractor workforces, including workforce planning, quality oversight, and budget management.
  • Strong understanding of the ML lifecycle and the role of human-labeled data in training, fine-tuning, and evaluating models.
  • Proficiency in SQL and Python for querying datasets, analyzing label quality, and building operational dashboards. At a minimum, be able to use AI tools to generate queries.
  • Excellent written and verbal communication skills - able to write rigorous technical specifications and present complex data concepts to both ML researchers and business stakeholders.
  • Proven ability to drive cross-functional alignment and exercise strong judgment, knowing when to champion collaborative efforts versus leading independently.
  • Exceptional critical thinking skills with a demonstrated capacity to navigate ambiguity and execute effectively in 0-to-1 environments without structured guidance.
  • Solid grasp of software development life cycles and hands-on experience leveraging AI-assisted tools like Cursor, Claude, or Gemini to accelerate workflows.

Nice to Have

  • Experience with LLM-based labeling pipelines, LLM-as-a-Judge evaluation, or human-AI calibration loops.
  • Familiarity with labeling platforms such as Label Studio, Scale AI, or Snorkel AI.
  • Experience evaluating 3D, games, videos, or multimodal data beyond standard text and image annotation.
  • Background in vendor platform evaluation or build vs. buy analysis.
  • Experience with data operations at a consumer platform operating at massive scale (100M+ users).
  • Knowledge of Roblox platform and its games, or general gaming experience.