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Annotation Judge Jobs in Illinois (NOW HIRING)

Arrive Logistics

Data Scientist II

Chicago, IL · On-site

... judgment to know when to apply each. * Familiarity with modern retrieval strategies and RAG ... Experience designing data annotation workflows, labeling guidelines, or label quality processes is ...

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Annotation Judge information

What is an annotation judge?

An Annotation Judge is a professional who evaluates the quality and accuracy of labeled data, such as text, images, or audio, which has been annotated for use in machine learning and artificial intelligence projects. Their main responsibility is to review, verify, and ensure that the data annotations meet specific guidelines and standards. Annotation Judges play a critical role in improving the reliability of training datasets, which directly impacts the performance of AI systems. They often work closely with data annotators, quality assurance teams, and project managers to maintain high data quality.

What are the key skills and qualifications needed to thrive as an annotation judge, and why are they important?

To thrive as an Annotation Judge, you need strong analytical skills, attention to detail, and subject matter expertise relevant to the data being evaluated, usually supported by a degree in a related field. Familiarity with annotation platforms, data labeling tools, and quality assurance systems is typically required. Excellent communication, impartiality, and critical thinking help you provide clear feedback and maintain high annotation standards. These skills are crucial to ensure data accuracy and consistency, which directly impact the performance of machine learning models.

What is the difference between Annotation Judge vs Data Annotator?

AspectAnnotation JudgeData Annotator
CredentialsTypically requires basic education, sometimes certification in data labelingUsually requires similar or less formal education, often on-the-job training
Work EnvironmentOffice or remote, working with data labeling platformsOffice or remote, performing data labeling tasks
Industry UsageUsed across AI, machine learning, and data science projectsCommon in AI, machine learning, and data preparation workflows
Search & Comparison IntentOften compared for roles involving data review and quality controlCompared for entry-level data labeling roles

The main difference between an Annotation Judge and a Data Annotator lies in their roles. Annotation Judges typically review and validate annotations made by Data Annotators, ensuring quality and accuracy. Data Annotators perform the initial labeling of data. Both roles are essential in AI data pipelines, with Annotation Judges focusing on quality control and Data Annotators on data preparation.

What are some common challenges faced by annotation judges, and how can they effectively overcome them?

Annotation Judges often face challenges such as maintaining impartiality, handling ambiguous or subjective data, and ensuring high consistency across large volumes of work. To overcome these, it’s essential to follow established guidelines closely, communicate regularly with team members for clarification, and participate in calibration sessions. Staying detail-oriented and seeking feedback can also help maintain accuracy and fairness in their assessments.
What are popular job titles related to Annotation Judge jobs in Illinois? For Annotation Judge jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Annotation Judge jobs in Illinois look for? The top searched job categories for Annotation Judge jobs in Illinois are:
What cities in Illinois are hiring for Annotation Judge jobs? Cities in Illinois with the most Annotation Judge job openings:
Annotation Judge jobs near you

Data Scientist II

Arrive Logistics

Chicago, IL • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 16 days ago

Arrive Logistics rating

4.0

Company rating: 4.0 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

Job description

Who We Are
Arrive Logistics is a leading transportation and technology company in North America with plans to grow significantly year over year. Our success is a testament to our remarkable team and what we're building together. We're committed to providing employees with a meaningful work experience and have established an award-winning culture that supports personal and career development in a fun, casual, and collaborative environment.
Who We Want
The Data Scientist II will work closely with Data Science, Product, and Engineering to build and improve ML and AI systems that drive operational value. This role is a great fit for a hands-on practitioner with applied experience in NLP and LLM-based systems who is ready to take on meaningful technical ownership. You'll contribute to the full lifecycle of production ML systems - from evaluation and measurement through development, deployment, and iteration - with a particular focus on text and language-based applications. The ideal candidate is comfortable operating in ambiguous problem spaces, can translate loosely defined business needs into concrete technical approaches, and communicates findings clearly to both technical and non-technical audiences.
What You'll Do
  • Develop, evaluate, and iterate on NLP and LLM-based systems, including text classification, information extraction, and context retrieval pipelines.
  • Build measurement and evaluation frameworks - both offline and online - to assess where and why systems are underperforming and quantify the impact of improvements.
  • Develop golden test datasets and define methodologies for creating and maintaining them over time, including designing annotation guidelines and ensuring label quality.
  • Evaluate and apply the appropriate approach for language tasks - whether prompt engineering, fine-tuning, or classical NLP methods - including modern retrieval and RAG architectures and LLM evaluation methodologies, based on the problem and available data.
  • Perform structured analysis of system performance to surface failure modes, data gaps, and high-value areas for investment, applying sound statistical reasoning to evaluation results.
  • Partner with engineers to support deployment, integration, and monitoring of ML and AI systems in production.
  • Contribute to standards and best practices around deploying, evaluating, and monitoring text and language-based ML systems.
  • Document work clearly and maintain knowledge artifacts that make systems understandable and maintainable over time.
  • Collaborate with senior data scientists and cross-functional partners to translate business needs into well-scoped technical solutions, including communicating findings and recommendations to non-technical stakeholders.

Qualifications
  • Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2-4 years of applied ML or data science experience, or equivalent practical experience.
  • Hands-on experience building or improving NLP or LLM-based systems in applied settings.
  • Familiarity with text classification, information extraction, or other NLP tasks - and an understanding of where these systems fail.
  • Experience with both prompt engineering and fine-tuning approaches for language tasks, with the judgment to know when to apply each.
  • Familiarity with modern retrieval strategies and RAG architectures and how they affect LLM system performance.
  • Experience with Hugging Face Transformers for text classification or related NLP tasks.
  • Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems, including comfort with statistical methods for measuring model performance.
  • Proficiency in Python and SQL, and comfort working with structured and unstructured data.
  • Ability to operate effectively in ambiguous problem spaces - scoping technical approaches when requirements are not fully defined.
  • Strong written communication skills; able to document systems and findings clearly and present recommendations to non-technical stakeholders.
  • Experience designing data annotation workflows, labeling guidelines, or label quality processes is a plus.
  • Experience with model deployment, monitoring, or production ML workflows is a plus.
  • Familiarity with LangChain and LangSmith or similar LLM orchestration and observability tooling is a plus.
  • Transportation or logistics industry experience is a plus.

The Perks of Working With Us
  • Take advantage of our comprehensive benefits package, including medical, dental, vision, life, disability, and supplemental coverage.
  • Invest in your future with our matching 401(k) program.
  • Build relationships and take part in learning opportunities through our Employee Resource Groups.
  • Enjoy office wide engagement activities, team events, happy hours and more!
  • Leave the suit and tie at home; our dress code is casual.
  • Work in the heart of downtown Chicago, IL!
  • Sweat it out at the LifeStart gym in our office building that includes brand new Peloton bikes, top-of-the-line equipment and personal training options.
  • Maximize your wellness with free counseling sessions through our Employee Assistance Program
  • Take time to manage your physical and mental health - we offer company paid holidays, paid vacation time and wellness days.
  • Receive 100% paid parental leave when you become a new parent.
  • Get paid to work with your friends through our Referral Program!
  • Get relocation assistance! If you are not local to the area, we offer relocation packages.

$130,000 - $150,000 a year
The base salary range for this position is $130K - $150K, plus bonus and benefits. The range displayed on each job posting reflects the pay range for the position across all locations. Within the range, individual pay is determined based on work location, job-related skills, experience, relevant education or training.
Your Arrive Experience
When we say "award-winning culture," we mean it. We've been recognized as a top workplace by Inc. Fast Company, Fortune, and earned Top Workplaces and Great Place to Work, to name a few. We intend on topping many more of those lists in the years to come, but we're not in it for the trophies. We're committed to culture because it keeps us connected to each other and invested in our shared success while having a blast along the way. Our employee-founded resource groups create communities within Arrive's walls, including Women in Logistics, Emerging Professionals, Prisms, Black Logistics Group, Salute and Unidos.
Notice:
To ensure a safe and transparent interview process, we want to note that Arrive Logistics adheres to strict recruitment practices. Candidates undergo an interview process, and Arrive Logistics does not provide unsolicited job offers. If you have concerns about receiving a fraudulent offer, please contact [email protected] for verification.

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