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Text Annotation Jobs in Martinez, CA (NOW HIRING)

Sieve, Inc

Product Operations Lead

San Francisco, CA · On-site

... images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal ... Source, onboard, and manage a distributed human workforce for data annotation, curation, and ...

Apple

ML Safety Engineer

San Francisco, CA · On-site

Experience working with large datasets, annotation tools, and model evaluation pipelines ... Ability to interpret unstructured data (text, transcripts, user sessions) and derive meaningful ...

Apple

ML Safety Engineer

San Francisco, CA · On-site

$184K - $277K/yr

Experience working with large datasets, annotation tools, and model evaluation pipelines ... Ability to interpret unstructured data (text, transcripts, user sessions) and derive meaningful ...

HRB

Product Engineer - Full Stack

San Francisco, CA · On-site

Advanced annotation tools, workflow automation, and quality control systems that enable teams to ... text, images, audio, and video. * Build new features by working with a cross-functional team to ...

Troveo AI Inc

Senior Product Manager

San Francisco, CA · On-site +1

$140K - $180K/yr

... text, and business workflows. Troveo indexes, enriches, and packages this high-quality data into ... annotation, and downstream delivery. * Help design processing pipelines and logic tailored to ...

EvenUp

Product Manager, AI Entities

San Francisco, CA · Remote

$177K - $209K/yr

Own the roadmap for a key part of our AI Platform, potentially including RAG services, text and ... Drive decisions on model performance, annotation strategy, and integration with continuous ...

EvenUp

Product Manager, AI Entities

San Francisco, CA · On-site

Own the roadmap for a key part of our AI Platform, potentially including RAG services, text and ... Drive decisions on model performance, annotation strategy, and integration with continuous ...

EvenUp Inc

Product Manager, AI Entities

San Francisco, CA · On-site

$177K - $209K/yr

Own the roadmap for a key part of our AI Platform, potentially including RAG services, text and ... Drive decisions on model performance, annotation strategy, and integration with continuous ...

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Text Annotation information

What is a text annotation?

A Text Annotation job involves labeling and categorizing text data to help train machine learning models. Annotators add tags, metadata, or classifications to text, enabling AI systems to understand language patterns. This work is essential for applications like chatbots, search engines, and sentiment analysis. Strong attention to detail and language proficiency are key skills for this role.

What are the typical day-to-day responsibilities for someone working in text annotation?

Text Annotation professionals spend much of their day reading and labeling text data according to specific guidelines, ensuring that information is correctly categorized and flagged. This can involve highlighting entities, identifying sentiments, tagging parts of speech, or annotating complex relationships within text documents. They frequently collaborate with project managers, data scientists, and quality assurance teams to clarify instructions and maintain data consistency. The role often involves independent work, but regular check-ins and feedback sessions help maintain accuracy and enhance understanding of evolving annotation requirements. This combination of independent and collaborative tasks makes the position dynamic and integral to successful AI or NLP project outcomes.

What are the key skills and qualifications needed to thrive in text annotation, and why are they important?

Strong language proficiency, attention to detail, and critical thinking are essential skills for succeeding as a Text Annotation specialist, often supported by a bachelor's degree in linguistics, computer science, or a related field. Familiarity with annotation tools like Labelbox, Prodigy, or the Amazon Mechanical Turk platform, as well as knowledge of data privacy and handling protocols, is typically required. Excellent communication, self-motivation, and the ability to focus on repetitive tasks help individuals excel in this position. These capabilities ensure high-quality, consistent data labeling for machine learning models, supporting the development of cutting-edge AI solutions.

What job categories do people searching Text Annotation jobs in Martinez, CA look for?

The top searched job categories for Text Annotation jobs in Martinez, CA are:

What cities near Martinez, CA are hiring for Text Annotation jobs?

Cities near Martinez, CA with the most Text Annotation job openings:

Text Annotation jobs near you
Infographic showing various Text Annotation job openings in Martinez, CA as of August 2026, with employment types broken down into 3% Internship, 53% Full Time, 29% Part Time, 4% Temporary, and 11% Contract. Highlights an 77% In-person, 3% Hybrid, and 20% Remote job distribution.

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

San Francisco, CA • On-site, Remote

Liquid AI, Inc
Library and Information Services • 11 - 50 employees
Learn more about Liquid AI, Incexternal

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 14 days ago

Key responsibilities

  • Own VLM post-training projects end-to-end, including translating customer requirements, designing workflows, and delivering evaluations.

  • Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation and synthetic data generation.

  • Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models, and interpret evaluation results.

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.

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