Human Data Quality Engineer (Founding Team) Prolific Prolific isn't just enabling AI innovation ... Work with clients as a strategic thought partner, challenging annotation schemas and data ...
Human Data Quality Engineer (Founding Team) Prolific Prolific isn't just enabling AI innovation ... Work with clients as a strategic thought partner, challenging annotation schemas and data ...
Strategic Projects Lead, Managed Services
North, SC · On-site +1
Work with Data, Product, and Engineering to identify automation opportunities, improve task ... Nice to have * Experience in data labeling, annotation, rater workflows, or evaluation tasks.
Strategic Projects Lead, Managed Services
North, SC · On-site +1
Work with Data, Product, and Engineering to identify automation opportunities, improve task ... Nice to have * Experience in data labeling, annotation, rater workflows, or evaluation tasks.
Data Annotation Engineer information
See Lexington, SC salary details
$44.1K - $55.4K
2% of jobs
$55.4K - $66.7K
9% of jobs
$74.6K is the 25th percentile. Wages below this are outliers.
$66.7K - $78K
20% of jobs
$78K - $89.4K
4% of jobs
$89.4K - $100.7K
4% of jobs
$100.7K - $112K
1% of jobs
$112K - $123.3K
0% of jobs
$123.3K - $134.7K
0% of jobs
The median wage is $140.3K / yr.
$134.7K - $146K
18% of jobs
$146K - $157.3K
0% of jobs
$161.7K is the 75th percentile. Wages above this are outliers.
$157.3K - $168.6K
41% of jobs
$44.1K
$126.2K
$168.6K
How much do data annotation engineer jobs pay per year?
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?
What is the highest salary for data annotator?
What is a data annotation engineer?
How hard is it to get hired by data annotation?
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.

Job description
Prolific
Prolific isn't just enabling AI innovation - we're redefining it. While foundational AI technologies are becoming commoditized, Prolific's human data infrastructure provides the high-quality, diverse data required to train the next generation of AI models. Through our platform, we empower researchers and companies to access a global, ethically curated participant base, ensuring cutting-edge AI research and training grounded in inclusivity and precision.
The Role
As one of the founding members of Prolific's newly formed AI Data Services team, you'll help build the quality systems behind some of the world's most advanced AI models. Data quality is a strategic priority for Prolific, so this is a high-visibility role with direct exposure to senior stakeholders. This isn't a traditional QA role. We are not looking for someone to review data against a predefined checklist. We are looking for an innovative thinker that can leverage their expertise to define what good means where no definition exists yet.
Acting as a strategic thought partner, you'll work at the intersection of human data, machine learning, evaluation across frontier use-cases that define what high-quality human data looks like for the next generation of advanced AI. This means that much of the work involves novel problems with no established answer, so you'll be comfortable working through ambiguity.. .
Your primary focus is working directly with clients and alongside frontier AI labs, translating what their models need into robust human data and evaluation strategies. Rather than checking quality at the end of a project, you'll engineer quality into every stage of the lifecycle, from study design and participant strategy through to evaluation, launch readiness and client delivery.You will also work alongside our product engineering, and supply teams to define and build the quality infrastructure that will enable us to deliver high quality human data at scale.
Much of the work you'll tackle won't have an existing playbook. You'll help create it.
What You'll Be Doing
- Design the quality frameworks that underpin complex human data programmes, from evaluation rubrics through to launch readiness.
- Work with clients as a strategic thought partner, challenging annotation schemas and data requirements when they won't produce the signal the model needs.
- Advise on project design and how the choice of schema can impact data quality.
- Build quality upstream across the operational workflow, from recruitment, screening, and training through to writing guidelines and running calibration sessions.
- Build scalable quality systems, measurement frameworks and automated checks using Python and SQL.
- Partner with product and engineering to build the quality infrastructure that delivers high-quality human data at scale.
- Investigate data quality and integrity issues, identifying root causes and turning insights into scalable improvements.
- Architect and build dashboards, monitoring and reporting that provide clear visibility into quality and operational performance.
- Raise the quality capability across the company, upskilling operations and acting as a thought mentor to junior analysts.
- Help define how Prolific approaches quality across new AI domains, shaping best practice as the team grows.
What You'll Bring to the Role
- 5+ years of experience in building quality, evaluation or annotation systems within AI, machine learning, LLMs or human data environments.
- Strong Python and SQL skills, with a passion for using data to solve complex quality problems.
- A solid understanding of machine learning pipelines and how human data impacts model performance.
- Strong analytical and statistical thinking, with experience designing scalable quality frameworks.
- The confidence and credibility to interact with stakeholders at frontier labs and act as a partner.
- The ability to leverage your experience and expertise to influence and guide stakeholders at every level, both client side and internally. in
- The ability to turn your own data analysis and quality methodology into requirements that product and engineering can build into systems.
- The ability to explain yout data analysis and findings clearly to non-technical stakeholders, so they can act on them.
- A proactive, builder's mindset -you enjoy creating new systems, navigating ambiguity and improving how things work.
Even Better if you have: - Experience with LLM evaluation, RLHF, AI safety or red teaming.
- Experience translating vague model or evaluation goals into clear annotation specifications.
- Experience working with human annotation programmes or human data operations.
- Familiarity with calibration, inter-rater agreement, drift detection or other evaluation methodologies.
Why Prolific is a great place to work
We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioral data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.
We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviors into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation - one that reflects the breath and the best of humanity.
Working for us will place you at the forefront of AI innovation, providing access to our unique human data platform and opportunities for groundbreaking research.
Join us to enjoy a competitive salary, benefits, and remote working within our impactful, mission-driven culture. At Prolific, our compensation packages for eligible roles include base salary, equity, and benefits. Many roles also include the opportunity to earn a cash variable element, such as a bonus or commission. Each job posting shows a salary range that reflects the minimum and maximum target for new hires, based on the role's location as well as your skills, experience, and relevant education or training. Your recruiter will also be happy to share the specific salary range for your preferred location during the hiring process.
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