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Day Image Annotation Jobs in California (NOW HIRING)

Character.ai

Research Engineer, Multimodal

Redwood City, CA · On-site

$225K - $400K/yr

... day. The Multimodal team is responsible for training, fine-tuning, and deploying cutting-edge image ... Design and build large-scale data pipelines and automated annotation workflows to support ...

Eventual

Software Engineer, Product

San Francisco, CA

... level annotation, and visualizations over corpus composition. * Design and build the APIs that ... Experience with video, image, or other multimodal content in the browser. * Background in developer ...

Hark

Data Engineering Lead

San Jose, CA

$170K - $450K/yr

You build for the day-2 case, not just the demo. * A quality instinct. You don't just move data ... Familiarity with multimodal data formats and processing pipelines (audio, video, image)

Hark

Data Engineering Lead

San Jose, CA · On-site

$170K - $450K/yr

You build for the day-2 case, not just the demo. * A quality instinct. You don't just move data ... Familiarity with multimodal data formats and processing pipelines (audio, video, image)

Planet

Visiting Staff Scientist

San Francisco, CA · On-site +1

... and reduce the annotation burden for time-sensitive mapping tasks. * Academic & Technical ... Deep Domain Expertise: 12+ years of experience in remote sensing and satellite image analysis, with ...

WPP Production

Program Coordinator

Sunnyvale, CA · On-site

$51 - $57/hr

That's why we've adopted a hybrid approach, with teams in the office around three days a week. If ... Responsible for briefing of projects to production studio and ensuring clear annotation and ...

WPP Production

Program Coordinator

Sunnyvale, CA · On-site

$51 - $57/hr

That's why we've adopted a hybrid approach, with teams in the office around 3 days a week. If you ... Responsible for briefing of projects to production studio and ensuring clear annotation and ...

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All JobsDay Image Annotation JobsDay Image Annotation Jobs in California

Day Image Annotation information

What is day image annotation?

Day image annotation is the process of labeling or tagging objects, regions, or attributes in images taken during daylight conditions. This annotated data is often used to train computer vision models, such as those used in autonomous vehicles or surveillance systems, to recognize and interpret visual information. Annotators may identify and mark features such as pedestrians, vehicles, road signs, and other elements visible in daytime imagery. The accuracy and quality of these annotations are crucial for developing reliable AI systems. Day image annotation can be performed manually or with the assistance of annotation tools.

What is the difference between Day Image Annotation vs Day Data Labeling?

AspectDay Image AnnotationDay Data Labeling
Primary FocusAnnotating images for machine learning modelsLabeling data, including images, for training AI systems
Work EnvironmentRemote or on-site, involving detailed image workSimilar, often remote, involving data categorization
Required SkillsAttention to detail, familiarity with annotation toolsAttention to detail, understanding of data structures
Industry UsageAutonomous vehicles, healthcare, retailAutonomous vehicles, healthcare, retail

Both Day Image Annotation and Day Data Labeling involve preparing data for AI models, but image annotation specifically focuses on marking objects within images, while data labeling can include various data types. They share similar skills and work environments, often overlapping in industries like autonomous vehicles and healthcare.

What are some common challenges faced by Day Image Annotation specialists, and how can they overcome them?

Day Image Annotation specialists often encounter challenges such as maintaining high accuracy while labeling large volumes of images and dealing with ambiguous or low-quality visual data. To overcome these challenges, it is important to follow clear annotation guidelines, communicate regularly with team members or project managers for clarifications, and utilize annotation tools efficiently. Many teams also conduct peer reviews to ensure consistency and quality across datasets, which helps specialists learn and improve their skills over time.

What are the key skills and qualifications needed to thrive as a Day Image Annotation Specialist, and why are they important?

To thrive as a Day Image Annotation Specialist, you need strong attention to detail, visual acuity, and a basic understanding of data labeling concepts, often supported by a high school diploma or equivalent. Familiarity with annotation software tools such as Labelbox, CVAT, or Supervisely is typically required. Strong organizational skills, patience, and the ability to work independently make someone stand out in this position. These skills ensure high-quality, accurate annotations that are essential for effective machine learning model training and data integrity.
What are the most commonly searched types of Image Annotation jobs in California? The most popular types of Image Annotation jobs in California are:
What are popular job titles related to Day Image Annotation jobs in California? For Day Image Annotation jobs in California, the most frequently searched job titles are:
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Day Image Annotation jobs near you

Member of Technical Staff - Post Training, Applied (Vision)

Liquid AI, Inc

San Francisco, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 15 days ago

Job description

About Liquid AI
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
The Opportunity
This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development.
Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how vision-language models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's multimodal post-training stack.
If you care about visual understanding, data quality, evaluation, and making VLMs actually work in production, this is a chance to shape how applied multimodal AI is done at a foundation model company.
What We're Looking For
We need someone who:
  • Takes ownership: Owns VLM post-training projects end-to-end, from customer requirements through delivery and evaluation.
  • Thinks end-to-end: Can reason across visual data curation, training, alignment, and evaluation as a single system.
  • Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory.
  • Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed.

The Work
  • Act as the technical owner for enterprise customer VLM post-training engagements.
  • Translate customer requirements into concrete multimodal post-training specifications and workflows.
  • Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks.
  • Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models.
  • Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret results and feed learnings back into core post-training pipelines.

Desired Experience
Must-have:
  • Hands-on experience with data generation and evaluation for VLM or multimodal post-training.
  • Experience training or fine-tuning vision-language models using SFT, preference alignment, and/or RL.
  • Strong intuition for visual data quality, annotation design, and multimodal evaluation.
  • Familiarity with vision encoders, image-text architectures, and how visual representations interact with language model backbones.

Nice-to-have:
  • Experience with visual grounding, document understanding, OCR, or video understanding tasks.
  • Experience contributing to shared or general-purpose multimodal post-training infrastructure.
  • Prior exposure to customer-facing or applied ML delivery environments.
  • Familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting.

What Success Looks Like (Year One)
  • Independently owns and delivers enterprise VLM post-training projects with minimal oversight.
  • Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality on multimodal workloads.
  • Has made durable contributions to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development.

What We Offer
  • Real ML work: You will fine-tune vision-language models, generate multimodal data, and ship solutions, not configure API calls. Your work feeds directly back into our core model development.
  • Compensation: Competitive base salary with equity in a unicorn-stage company.
  • Health: We pay 100% of medical, dental, and vision premiums for employees and dependents.
  • Financial: 401(k) matching up to 4% of base pay.
  • Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.

Apply