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Full Time Machine Learning Data Annotation Jobs in Seattle, WA

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Participate in the full development cycle: data collection, labeling, model development ...

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ... Data Analysis and Insight Generation : Analyze experimental data to extract actionable insights.

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class ... Maintain awareness of industry best practices for data maintenance handling as it relates to your ...

This role requires deep technical expertise in advanced data analytics and machine learning, as well as a hands-on approach to designing, building, and optimizing ML solutions that power user-facing ...

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class ... Maintain awareness of industry best practices for data maintenance handling as it relates to your ...

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

Responsibilities : • Study and transform data science prototypes • Design machine learning systems • Research and implement appropriate ML algorithms and tools • Develop machine learning ...

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

Key job responsibilities As a Software Development Engineer in Machine Learning, you will: - Design, build, and operate near-real-time (NRT) data ingestion pipelines using Apache Flink, Kinesis, and ...

We are actively involved in the whole ML cycle from data collection design and data processing to ... machine learning or related field PhD in computer vision, computer graphics, machine learning ...

Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying ... Hands on expertise in Machine Learning models using R/Python, SQL, well versed in statistical ...

Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying ... Hands on expertise in Machine Learning models using R/Python, SQL, well versed in statistical ...

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Full Time Machine Learning Data Annotation information

See Seattle, WA salary details

$42.7K

$139.7K

$223.6K

How much do full time machine learning data annotation jobs pay per year?

As of Jul 22, 2026, the average yearly pay for full time machine learning data annotation in Seattle, WA is $139,680.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,100.00 and $154,800.00 per year, depending on experience, location, and employer.

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

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What are Full Time Machine Learning Data Annotation jobs?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Seattle, WA? The most popular types of Machine Learning Data Annotation jobs in Seattle, WA are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Seattle, WA look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Seattle, WA are:
Machine Learning Engineer, Information Security

Machine Learning Engineer, Information Security

Apple

Seattle, WA

$142K - $263K/yr

Full-time

Medical, Dental, Retirement

Posted 17 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 673 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

Join Apple’s Information Security Machine Learning (ISML) team, where we are redefining cybersecurity through data-driven intelligence. Our mission is to transform traditional reactive security measures into autonomous systems that proactively detect and defend against threats. We achieve this through cutting-edge research, applied science, and robust infrastructure development. We are seeking a highly motivated and talented Machine Learning Engineer to join our dynamic and growing team. You will play a pivotal role in designing, developing, and deploying machine learning models that power our advanced security products and services. This is an incredible opportunity to make a real world impact by building intelligent systems that detect and prevent advanced threats, enhance critical security processes, and protect Apple and our customers.
Description
The Security ML Engineer will bring their expertise in machine learning to the problems and opportunities facing Information Security at Apple. You will contribute to the Autonomous Security program by developing production ready AI/ML systems using Apple’s internal platforms, cloud services, and local compute environments.
You will translate research to design, building and deploying machine learning models for security use cases, leveraging generative AI, statistical modeling, reinforcement learning, and data science to address complex security challenges. You will collaborate with cross-functional teams including security teams, software engineers, and researchers to prototype and scale AI/ML driven security solutions. You will own end-to-end ML workflows: data exploration, model development, evaluation metrics design, deployment, and monitoring.
Preferred Qualifications
Ph.D. in a technical field such as Computer Science, Engineering, Statistics, or related disciplines.
In-depth knowledge of ML algorithms, including supervised/unsupervised learning, deep learning (CNNs, RNNs, LSTMs), and large language models.
Industry experience in deploying ML and generative AI solutions in cybersecurity contexts.
Familiarity with cloud platforms (e.g., AWS, GCP) and their security offerings is a plus.
Experience with large scale data processing and analysis using tools such as Apache Spark.
Experience working in a key security process, such as Incident Response, Threat Intelligence, or Vulnerability Management.
Experience with specific security tools and technologies (e.g., SIEM, IDS/IPS, endpoint security solutions).
Contributions to open-source security or machine learning projects.
Publications or talks at top-tier ML or security conferences.
Additional proficiency in C++ or Swift is a plus
Minimum Qualifications
BSc or Masters degree in Machine Learning, Data Science, Computer Science, Information Security, Mathematics, Statistics, or related field.
Strong programming skills in Python and Scala; experience with ML libraries such as TensorFlow, PyTorch, HuggingFace, and Scikit-learn.
Hands-on experience with full ML model lifecycle: from experimentation to deployment and monitoring.
Solid grasp of security fundamentals including network security, incident response, threat modeling, and vulnerability management.
Excellent written and verbal communication skills, with the ability to present technical concepts clearly to varied audiences.
Familiarity with CI/CD workflows and ML pipelines .
Experience operating, and scaling production services in cloud native environments.
Experience deploying models on CUDA devices using tools like TensorFlow or Torch.
Proven experience building generative AI applications for real-world use cases.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

What Apple employees say

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976