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Data Annotation Engineer Jobs in Seattle, WA (NOW HIRING)

LVT

Staff Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to ... Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality ...

LVT

Staff Data Engineer

Seattle, WA

Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to ... Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality ...

Handshake

AI Red Teamer (LLM Generalist)

Seattle, WA · On-site

... Collaborate with engineers, data scientists, and researchers to share findings and strengthen ... with structured data annotation and rubric-based scoring • Prior work in trust and safety ...

Apple

AIML - Data Scientist, Evaluation

Seattle, WA

$139K - $258K/yr

You will partner with various engineering teams to improve search quality and guide feature ... Worked with methods that address accuracy and variability in human annotation data. Ability to ...

Apple

AIML - Sr Data Scientist, Evaluation

Seattle, WA

$139K - $258K/yr

You will partner with various engineering teams to improve search quality and guide feature ... Worked with methods that address accuracy and variability in human annotation data. Ability to ...

Handshake

AI Red Teamer, LLM Generalist

Seattle, WA · On-site

$32 - $95/hr

Collaborate with engineers, data scientists, and researchers to share findings and strengthen ... Comfort with structured data annotation and rubric-based scoring * Prior work in trust and safety ...

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All JobsData Annotation Engineer JobsData Annotation Engineer Jobs in Seattle, WA

Data Annotation Engineer information

See Seattle, WA salary details

$58.6K

$167.8K

$224.2K

How much do data annotation engineer jobs pay per year?

As of Jul 1, 2026, the average yearly pay for data annotation engineer in Seattle, WA is $167,814.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,600.00 and $223,100.00 per year, depending on experience, location, and employer.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

Does data annotation really pay?

Data annotation engineers can earn competitive wages, often paid hourly or per task, with pay rates varying based on experience, complexity of annotations, and the platform or employer. Entry-level roles may start at minimum wage, while experienced annotators or those with specialized skills can earn higher salaries or freelance rates. Overall, data annotation can provide a reliable income, especially for remote or flexible work arrangements.

What is the highest salary for data annotator?

The highest salary for a data annotation engineer can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of annotation tasks. Senior roles or those with specialized skills in tools like Labelbox or CVAT may earn higher compensation. Salaries vary widely across companies and regions but generally reflect the technical skills required for high-quality data labeling.

What is a data annotation engineer?

A data annotation engineer is a professional responsible for labeling and annotating data, such as images, text, or videos, to prepare it for machine learning models. They often use specialized tools and follow guidelines to ensure data quality, supporting the development of AI systems.

How hard is it to get hired by data annotation?

Getting hired as a data annotation engineer typically requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require advanced degrees, but strong accuracy and consistency are important for success in the role.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are popular job titles related to Data Annotation Engineer jobs in Seattle, WA? For Data Annotation Engineer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Data Annotation Engineer jobs in Seattle, WA look for? The top searched job categories for Data Annotation Engineer jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Data Annotation Engineer jobs? Cities near Seattle, WA with the most Data Annotation Engineer job openings:
Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in Seattle, WA as of June 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $167,814 per year, or $80.7 per hour.

Staff Data Engineer

LVT

Seattle, WA • On-site

$130K - $156K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 10 days ago

Job description

ABOUT LVT
LVT is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first mobile, solar-powered units, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions-from our physical units in the field to a powerful Agentic AI platform-that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations. This is your chance to join a cutting-edge team that isn't just watching the world change, but actively building the technology that is changing it.
We're a team that's focused on growth and innovation, and we're proud that our crew, products, and leadership are being recognized for it.
  • A Top-Tier Growth Company: Named one of the Financial Times' Fastest Growing Companies 2025 and #10 on the Inc. 5000 Rocky Mountain Regional list for 2025.
  • Innovative Leadership: Our CEO, Ryan Porter, was named an EY Entrepreneur of the Year 2025, and our CTO, Steve Lindsey, was inducted into the Silicon Slopes CTO Hall of Fame in 2024.
  • Product & Software Excellence: We were named one of The Software Report's Top 100 Software Companies of 2023 and are a winner of the Security Today Govies Award for 2025.

ABOUT THIS ROLE
LVT's AI systems are only as good as the data behind them. As we move toward Physical AI, the binding constraint shifts from model architecture to the data flywheel.
We are seeking a Staff Data Engineer to own that flywheel end to end including logs, sensor telemetry, labels and annotations, evaluation and benchmark sets. Every AI team trains and evaluates from a single stack that transforms data from the raw source through standardized, versioned, governed datasets.
This is a senior individual-contributor and technical-leadership role; formal people management is not required. You will partner closely with AI/ML research, the ML platform / MLOps function. You own the data side of the contract that defines what a model consumes and emits and annotation, edge, and infrastructure teams. You should be equally comfortable discussing dataset schema design, storage and partitioning trade-offs for multimodal data, versioning and migration strategy, and the governance controls that keep sensitive video and sensor data safe.
ROLE RESPONSIBILITIES
  • Data Flywheel Ownership: Own the end-to-end loop that converts raw edge telemetry and video into labeled training data, frozen evaluation sets and feeds model outputs back into the next round.
  • Layered Dataset Pipelines: Build and own the pipelines that register raw source data, standardize it into a single well-defined schema, and join and aggregate it into curated datasets so every team trains, validates, and benchmarks from one consistent store through one reader, rather than copying and reformatting data per use case.
  • Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to datasets without rewriting source data, then versioned, quality-checked, and served, partnering with annotation and data-operations teams on label production and verification while you own the dataset, storage, and serving side.
  • Evaluation & Benchmark Sets: Own the frozen, versioned validation and benchmark datasets that make model comparisons valid over time stable enough that an accuracy delta reflects the model, not a shifting dataset including the review and scrubbing discipline required before any set is shared externally.
  • Dataset Versioning: Own schema and content versioning so producers can evolve datasets without breaking consumers opt-in versions, append-without-rewrite for new fields, and the reader/writer indirection that lets data migrate underneath clients on a controlled rollout instead of forced lockstep migrations.
  • Framework Integration & Self-Serve Access: Own the read/write libraries and integrations researchers depend on PyTorch/Lightning dataloaders, a simple record-level CRUDL API, and Spark/analytics access and self-service so AI teams stay focused on model development.
  • Governance Enforced: Make governance machine-enforced in the flywheel rather than documented after the fact classification of clips, frames, labels, and embeddings; scrubbing and anonymization in load jobs; and lineage and provenance for every dataset version, annotation campaign, and training input.
  • Technical Mentorship: Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality gates and mentor IC work toward them, growing the function as the team forms.

OUR IDEAL CANDIDATE
  • Data Engineering Depth: 8+ years building and operating large-scale data pipelines and data-lake or lakehouse systems in production ingestion, ETL/ELT, partitioning and storage-format decisions, and the reader/writer libraries consumers rely on.
  • ML Data Specialty: Has built data pipelines for model training and evaluation, labeled data, and evaluation/benchmark sets with a working understanding of how data quality and versioning move model results.
  • Lakehouse Architecture: Strong experience with medallion-style layered data architectures and modern table/lake formats (e.g. Iceberg, Delta, Parquet, or comparable), including schema evolution and dataset versioning.
  • Multimodal Data at Scale: Experience with large multimodal data video, image, sensor/telemetry and the storage and access patterns that make it queryable at scale (denesting, repartitioning, binary-inline vs. reference storage).
  • Framework Integration: Hands-on with the data side of ML frameworks PyTorch/Lightning dataloaders and Spark and strong Python knowledge.
  • Governance & Provenance: Practical experience enforcing data governance in pipelines classification, access control, lineage and provenance, retention, particularly for privacy sensitive data.
  • Technical Leadership: A track record of setting data-engineering direction and leveling up engineers (technical leadership; formal management not required).
  • Education: Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent practical experience.

PREFERRED QUALIFICATIONS
  • Streaming or near-real-time ingestion from edge/IoT sources into a data lake (e.g. Kafka, Lambda, EMR, or similar).
  • Append-without-rewrite and hash-indexed dataset techniques on open table formats, and dataset/feature-versioning systems.
  • Generative-AI data work: fine-tuning and evaluation dataset curation for LLMs/VLMs.
  • Exposing datasets to AI agents through MCP-style query interfaces, with semantic schema and plain-language documentation for retrieval.
  • Computer-vision / video annotation tooling and workflows (e.g. Encord, Labelbox, or similar).

COMPENSATION
The beginning annual salary range for this role is $171,900 - $221,000 USD and is determined by location, job-related experience, and education/training. Your total earning potential is amplified by a bonus structure tied to meeting goals, and you will become an owner from day one through our employee equity program.
BENEFITS
We believe you do your best work when your whole life is supported. We invest in our crew's health, families, and financial futures with a benefits package designed to support you inside and outside the office. Full-time benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits (401k match up to 4%), and flexible PTO.
LVT IS PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. All candidates must pass a drug screening and background check upon employment. Some roles may also require passing a federal background check and fingerprinting. Must be authorized to work in the U.S. If reasonable accommodation is needed to participate in the job application or interview process, and/or to perform essential job functions, please reach out to your recruiter.

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