Senior AI Data Annotation Jobs in Palo Alto

Senior AI data annotation jobs in Palo Alto at Rex.zone focus on training data quality for modern AI/ML systems, including RLHF, data labeling, QA evaluation, and prompt evaluation for large language models. You will apply annotation guidelines compliance to improve model performance, support LLM training pipelines, and deliver consistent labels for NLP and computer vision tasks such as named entity recognition, classification, and content safety labeling. This remote, full-time role is designed for mid-senior professionals who can translate ambiguous requirements into high-quality annotations and measurable evaluation signals. Explore and apply through Rex.zone to work on production-grade datasets used by AI labs, tech startups, and annotation vendors.

Senior AI Data Annotation Jobs in Palo Alto

Title: Senior AI Data Annotation Specialist (Palo Alto) Date: 25-02-2026 Company: Rex.zone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Senior AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, content safety labeling, large language model evaluation, model performance improvement, LLM training pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

Role Overview

Own end-to-end annotation workflows for senior AI data annotation jobs in Palo Alto, including dataset scoping, label taxonomy design support, guideline refinement, and audit-ready documentation. Produce high-precision annotations and evaluation judgments used in RLHF pipelines, preference ranking, and prompt-response scoring to improve large language model evaluation and model performance improvement. Partner with engineering and research stakeholders to resolve edge cases, reduce ambiguity, and maintain training data quality across iterative releases.

Key Responsibilities

Deliver consistent data labeling across NLP and computer vision tasks, including named entity recognition, intent classification, summarization quality scoring, and image/video bounding boxes or segmentation when required. Execute QA evaluation (sampling plans, inter-annotator agreement checks, error taxonomy) and drive annotation guidelines compliance. Perform prompt evaluation and response assessment for helpfulness, correctness, safety, and policy adherence; support content safety labeling and red-teaming style data capture. Provide feedback loops to improve workflows, tooling, and instruction clarity; track annotation throughput and quality metrics aligned to LLM training pipelines.

Required Qualifications

Mid-senior experience in AI data annotation, data labeling operations, or evaluation programs for LLMs, NLP, or computer vision annotation. Demonstrated ability to interpret complex guidelines, make consistent judgments, and explain rationale with audit-friendly notes. Familiarity with RLHF concepts, rubric-based evaluation, and training data quality practices. Strong written communication, attention to detail, and comfort working asynchronously in a remote, full-time environment.

Preferred Qualifications

Experience with large language model evaluation frameworks, preference data collection, prompt-response ranking, and safety evaluations. Exposure to content safety labeling, policy-driven moderation datasets, or sensitive content handling. Experience with annotation QA methods such as gold sets, calibration sessions, and inter-annotator agreement measurement. Background collaborating with AI labs, tech startups, BPOs, or annotation vendors in production data pipelines.

Tools and Workflow

Work within Rex.zone-supported annotation and evaluation tooling, including task queues, rubric-based scoring interfaces, and QA sampling. Use structured issue tracking for guideline changes and edge-case resolution. Follow privacy and security best practices for handling training data, prompt logs, and model outputs.

What Success Looks Like

High training data quality with clear annotation guidelines compliance, low rework rates, and measurable reductions in critical labeling errors. Reliable QA evaluation outcomes with consistent calibration and documented decisions. Demonstrable contribution to model performance improvement through better large language model evaluation signals and well-structured RLHF datasets.

How to Apply on Rex.zone

Apply through Rex.zone with a resume highlighting AI data annotation, data labeling, RLHF or LLM evaluation experience, and examples of QA evaluation or guideline development. Include domain exposure (NLP, computer vision annotation, content safety labeling) and describe how you ensure training data quality in real-world LLM training pipelines.

Frequently Asked Questions

Q: Is this role remote even though it targets Palo Alto?
Yes. Remote Type is Remote. The Palo Alto keyword reflects the search and market focus, while day-to-day work is performed remotely within the US.
Q: What does “Senior AI data annotation” mean in practice?
It means producing high-accuracy labels and evaluation judgments while also influencing guidelines, resolving edge cases, running QA evaluation, and improving training data quality for LLM training pipelines.
Q: Will I work on RLHF tasks?
Yes. The role includes RLHF-related workflows such as preference ranking, rubric scoring, and prompt evaluation to strengthen large language model evaluation and model performance improvement.
Q: Which domains are covered: NLP, computer vision, or content safety?
The role can include NLP tasks (e.g., named entity recognition), computer vision annotation, and content safety labeling depending on project needs, with a strong emphasis on evaluation quality.
Q: Is this full-time and what is the salary range?
Yes, Employment Type is FULL_TIME. Salary range is USD 63360 to 126720 per year, with Pay Period YEAR.

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