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Data Annotation Law Jobs in California (NOW HIRING)

You will collaborate with technical teams to refine annotation tools and curate impactful data ... Criminal Law, Civil Litigation, Torts, International Law, Health Law, Civil Rights Law ...

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Data Annotation Law information

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

To thrive as a Data Annotation Specialist, you need keen attention to detail, strong analytical skills, and a basic understanding of data labeling protocols, often supported by a high school diploma or relevant training. Familiarity with data annotation tools like Labelbox, Supervisely, or CVAT, as well as knowledge of data privacy regulations, is typically required. Excellent communication, time management, and consistency are vital soft skills for collaborating with teams and maintaining annotation quality. These skills ensure accurate, reliable data labeling, which is critical for developing effective machine learning models.

What is the difference between Data Annotation Law vs Data Labeler?

AspectData Annotation LawData Labeler
CredentialsLegal knowledge, compliance certificationsBasic training, attention to detail
Work EnvironmentLegal offices, compliance departmentsData annotation platforms, remote or office settings
Industry UsageLegal, AI compliance, data privacyAI training, machine learning datasets
Search & ComparisonLegal roles, compliance in data annotationData labeling, data annotation jobs

Data Annotation Law involves legal expertise ensuring data annotation processes comply with laws and regulations, often requiring legal certifications. Data Labelers focus on annotating data for AI models, typically with basic training. While both roles work within data annotation, Data Annotation Law emphasizes legal compliance, whereas Data Labelers concentrate on data preparation for machine learning.

What is Data Annotation Law?

Data Annotation Law refers to the legal frameworks and regulations that govern the process of labeling, tagging, or categorizing data for use in machine learning and artificial intelligence applications. These laws address issues such as data privacy, intellectual property, consent, and the ethical use of annotated data. Data Annotation Law ensures that organizations handle data responsibly and comply with national and international standards when using human annotators or automated systems. It is crucial for companies to understand these legal requirements to avoid potential legal liabilities and protect the rights of data subjects.

What are some common challenges faced by professionals working in data annotation for the legal industry, and how can they be addressed?

Professionals in data annotation law often encounter challenges such as interpreting complex legal language, ensuring consistency and accuracy in labeling, and maintaining confidentiality with sensitive information. Collaboration with legal experts and ongoing training in legal terminology are essential to address these issues. Additionally, many organizations implement rigorous quality assurance processes and utilize annotation guidelines to help annotators navigate ambiguous cases and improve overall data quality.
What are popular job titles related to Data Annotation Law jobs in California? For Data Annotation Law jobs in California, the most frequently searched job titles are:
What job categories do people searching Data Annotation Law jobs in California look for? The top searched job categories for Data Annotation Law jobs in California are:
What cities in California are hiring for Data Annotation Law jobs? Cities in California with the most Data Annotation Law job openings:
Infographic showing various Data Annotation Law job openings in California as of July 2026, with employment types broken down into 2% Locum Tenens, 34% Full Time, 26% Part Time, 2% Contract, 35% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.
Sr. Software Analyst, Annotation Analytics, Autonomy

Sr. Software Analyst, Annotation Analytics, Autonomy

Rivian

Palo Alto, CA

$132K - $165K/yr

Full-time

Medical, Dental, Vision

Posted 3 days ago


Rivian rating

7.3

Company rating: 7.3 out of 10

Based on 158 frontline employees who took The Breakroom Quiz

20th of 44 rated automakers


Job description

About Rivian

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. 

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. 


Role Summary

In this role, you will analyze large-scale 2D/3D annotation datasets to identify data gaps,
quality issues, and operational inefficiencies, and build dashboards and metrics that
monitor annotation throughput, quality, cost, and vendor performance. You will partner
closely with Annotation Operations, Perception, and Engineering teams to ensure data
readiness for model training and evaluation.
The ideal candidate is data-driven, detail-oriented, and execution-focused, with a strong
understanding of annotation workflows and a passion for improving data quality and
efficiency at scale. This role plays a critical part in enabling informed decision-making,
continuous process improvement, and scalable annotation operations across internal
teams and external vendors


Responsibilities

Data Analysis, Insights & Dashboards
Analyze large-scale 2D/3D annotation datasets to identify data gaps, quality
issues, coverage gaps, and inefficiencies impacting Perception and ML training.
Design, build, and maintain dashboards and reports to monitor:

Annotation throughput, efficiency, and cost
Quality metrics (QA pass rates, error types, rework)
Vendor and annotator performance trends
Translate complex annotation and perception data into clear, actionable insights
for engineering, annotation operations, and leadership.
Partner with Perception teams to align data readiness metrics with model training
and evaluation needs.

Annotation Operations & Quality
Assess level of effort for annotation projects and support end-to-end execution,
including in-house vs. vendor decisions.
Collaborate with stakeholders to gather labeling requirements and define,
maintain, and evolve labeling policies.
Define and track annotation quality, productivity, and certification metrics;
continuously refine benchmarks.
Conduct QA/QC analysis on annotated data, identify systemic issues, and
provide structured feedback to annotators and vendors.
Implement and measure labeling efficiency improvements, using data to validate
impact.

Process Improvement & Automation
Identify opportunities to make data delivery and annotation workflows faster,
more accurate, and scalable.
Build lightweight automation (scripts, queries, data pipelines) to reduce manual
reporting and operational overhead.

Maintain structured datasets (databases, tables, metrics pipelines) to enable
consistent reporting and historical analysis.

Cross-Functional & Vendor Collaboration
Work closely with Annotation Ops, Perception, and Engineering teams to ensure
consistent process implementation.
Support vendor management by analyzing performance, cost, and quality metrics
across multiple annotation partners.
Prepare technical and operational reports to support planning, execution, and
decision-making.
Coordinate across teams and time zones to ensure alignment and timely delivery.


Qualifications

Required
BS degree in a technical or related field with 2+ years of relevant experience (or
5+ years equivalent industry experience).
2+ years supporting data annotation, ML data, or labeling-related projects.
Strong understanding of 2D/3D data annotation workflows for ML and Perception
use cases.
Hands-on experience with data analysis, metrics tracking, and dashboarding.
Working knowledge of Python, SQL, databases, and/or data visualization tools.
Experience analyzing LiDAR point clouds, video, and image annotation data.
Ability to manage multiple projects, prioritize effectively, and deliver under tight
timelines.

Strong written and verbal communication skills, with the ability to explain data
insights to non-technical stakeholders.

Nice to Have
Experience mentoring or guiding junior team members or offshore teams.
Familiarity with annotation QA frameworks and error taxonomy design.
Experience building or improving operational analytics for efficiency, cost, or
quality.
Comfort working in a fast-paced, cross-functional, and collaborative environment.


Pay Disclosure

Salary Range for California Based Applicants: $132,100-$165,100 (actual compensation will be determined based on experience, location, and other factors permitted by law).

Benefits Summary: Rivian provides robust medical/Rx, dental and vision insurance packages for full-time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premiums.



Equal Opportunity

Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law.

Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com.

Candidate Data Privacy

Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. 

Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. 

Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions.  

Please note that we are currently not accepting applications from third party application services.

Qualifications:

Required
BS degree in a technical or related field with 2+ years of relevant experience (or
5+ years equivalent industry experience).
2+ years supporting data annotation, ML data, or labeling-related projects.
Strong understanding of 2D/3D data annotation workflows for ML and Perception
use cases.
Hands-on experience with data analysis, metrics tracking, and dashboarding.
Working knowledge of Python, SQL, databases, and/or data visualization tools.
Experience analyzing LiDAR point clouds, video, and image annotation data.
Ability to manage multiple projects, prioritize effectively, and deliver under tight
timelines.

Strong written and verbal communication skills, with the ability to explain data
insights to non-technical stakeholders.

Nice to Have
Experience mentoring or guiding junior team members or offshore teams.
Familiarity with annotation QA frameworks and error taxonomy design.
Experience building or improving operational analytics for efficiency, cost, or
quality.
Comfort working in a fast-paced, cross-functional, and collaborative environment.

Education:UNAVAILABLEEmployment Type: FULL_TIME

What Rivian employees say

Pay

Benefits

Hours and flexibility

Workplace

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About Rivian

Sourced by ZipRecruiter

Rivian is a pioneering automotive industry player headquartered in Irvine, California. Established in 2009, the company has made notable advancements in developing sustainable transportation solutions. It is widely recognized for its electric adventure vehicles: the R1T pickup and the R1S SUV. Rivian is dedicated to creating a positive shift in societal mobility and emphasizes sustainability, innovation, and adventure as part of its core values. Their mission is to keep the world adventurous forever - a testament to their commitment in transitioning the world to sustainable transportation. Rivian's achievements are numerous, with one of the most notable being securing a significant multi-billion dollar investment from Amazon for the production of electric delivery vans.

Industry

Automobile dealers

Company size

10,000+ Employees

Headquarters location

Irvine, CA, US

Year founded

2009