The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external ... Utilize modern AI-assisted development tools (e.g., LLM-based coding agents) to accelerate ...
The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external ... Utilize modern AI-assisted development tools (e.g., LLM-based coding agents) to accelerate ...
Llm Annotation information
Which 5 jobs will survive AI?
Jobs involving LLM annotation, such as data annotators and labelers, are likely to persist as they require human judgment for complex or nuanced tasks. Roles that involve creative thinking, emotional intelligence, and strategic decision-making, like psychologists, teachers, healthcare professionals, and managers, are also expected to remain in demand despite AI advancements. These jobs often require skills that are difficult for AI to replicate fully.
How much do AI annotators make?
AI annotators, including those working as language model annotation specialists, typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the tasks. Some positions may offer hourly wages or project-based pay, with higher rates for specialized skills or advanced tools proficiency.
Are data annotations still hiring?
Data annotation roles, including those for large language models (LLMs), are currently in demand as companies continue to develop AI and machine learning systems. These jobs often require attention to detail and familiarity with annotation tools, and opportunities are available through various online platforms and companies expanding their AI teams.
What is an LLM annotator?
An LLM annotator is a person who labels and tags data to train large language models (LLMs). They review and annotate text data to improve model accuracy, often using specialized tools and following specific guidelines. This role requires attention to detail and understanding of language patterns.
What is the difference between Llm Annotation vs Data Labeler?
| Aspect | Llm Annotation | Data Labeler |
|---|---|---|
| Required Credentials | Basic computer skills, sometimes familiarity with AI tools | Basic skills, often on-the-job training |
| Work Environment | Remote or office-based, tech-focused | Remote or on-site, varied industries |
| Industry Usage | AI, machine learning, NLP projects | Various industries including marketing, healthcare, and tech |
| Search & Comparison Intent | Understanding roles in AI data preparation | General data labeling tasks |
In summary, Llm Annotation involves specialized annotation for large language models, often requiring familiarity with AI tools, while Data Labeler is a broader role focused on labeling data across multiple industries with minimal technical requirements.
What is LLM annotation?
LLM annotation refers to the process of labeling or tagging data specifically for training and evaluating large language models (LLMs) like GPT or BERT. Annotators read text and apply labels, correct errors, or provide feedback to help improve the model's understanding and performance. This work is crucial for supervised learning, as well-annotated datasets help LLMs better recognize patterns, context, and meaning in human language. LLM annotation can involve tasks such as sentiment analysis, named entity recognition, or instruction following. Annotators often use specialized platforms or tools to complete their tasks efficiently and accurately.
What are the key skills and qualifications needed to thrive as an LLM Annotation Specialist, and why are they important?
To thrive as an LLM Annotation Specialist, you need strong analytical skills, attention to detail, and a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, natural language processing (NLP) tools, and data labeling systems is typically required. Excellent communication, critical thinking, and the ability to follow guidelines precisely are valuable soft skills for this role. These skills ensure high-quality, accurate data annotation, which directly impacts the performance and reliability of large language models.
What are some common challenges faced by LLM Annotation specialists, and how can they be addressed?
LLM Annotation specialists often encounter challenges such as interpreting ambiguous language data, maintaining annotation consistency across complex datasets, and keeping up with evolving guidelines. These can be addressed by participating in regular team syncs to clarify guidelines, using annotation tools with built-in quality checks, and collaborating closely with project leads and fellow annotators. Continuous learning and open communication help ensure high-quality, reliable data annotation and support professional growth within the AI and NLP fields.
What are popular job titles related to Llm Annotation jobs in Tennessee? For Llm Annotation jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Llm Annotation jobs in Tennessee look for? The top searched job categories for Llm Annotation jobs in Tennessee are:
What cities in Tennessee are hiring for Llm Annotation jobs? Cities in Tennessee with the most Llm Annotation job openings:
Staff Software Engineer / Machine Learning Engineer - Radiology
Memphis, TN • On-site
Full-time
Posted 25 days ago
St. Jude Children's Research Hospital rating
8.6
Based on 12 frontline employees who took The Breakroom Quiz
41st of 1,051 rated hospitals
Job description
We are seeking a highly motivated and experienced Machine Learning Engineer to develop advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging. This role focuses on building robust algorithms for segmentation, quantification, and detection across CT, MRI, and X-ray.
This position sits at the center of a well-resourced, data-rich research environment with established infrastructure for multi-institutional data aggregation, curation, and large-scale annotation. St. Jude Children's Hospital has incredible high-performance computing resources. The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external engineering support, enabling this role to focus on high-impact model development, validation, and clinical translation.
Many projects are designed with a path toward regulatory clearance via FDA's 510(k) or De Novo pathways, and the successful candidate will work closely with regulatory and quality experts to support reproducible, well-documented, and clinically deployable AI solutions. This role offers a unique combination of academic productivity (authorship opportunities) and real-world impact through translation into clinical practice.
Machine Learning Engineer
Key Responsibilities
Minimum Education and/or Training:
Minimum Experience:
Preferred Qualifications
Academic and Career Development Opportunities
Key Attributes
Compensation
In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specific salary offer takes into account factors that are considered in making compensation decisions including but not limited to skill sets, experience and training, licensure and certifications, and other business and organizational needs. It is not typical for an individual to be hired at or near the top of the salary range and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current salary range is $104,000 - $186,160 per year for the role of Staff Software Engineer / Machine Learning Engineer - Radiology.
Explore our exceptional benefits!
St. Jude is an Equal Opportunity Employer
No Search Firms
St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude.
This position sits at the center of a well-resourced, data-rich research environment with established infrastructure for multi-institutional data aggregation, curation, and large-scale annotation. St. Jude Children's Hospital has incredible high-performance computing resources. The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external engineering support, enabling this role to focus on high-impact model development, validation, and clinical translation.
Many projects are designed with a path toward regulatory clearance via FDA's 510(k) or De Novo pathways, and the successful candidate will work closely with regulatory and quality experts to support reproducible, well-documented, and clinically deployable AI solutions. This role offers a unique combination of academic productivity (authorship opportunities) and real-world impact through translation into clinical practice.
Machine Learning Engineer
Key Responsibilities
- Develop, train, and validate state-of-the-art ML/DL models for segmentation, quantification, and detection across CT, MRI, and X-ray
- Design and implement 2D and 3D model architectures (CNNs, transformer-based, and foundational models)
- Build scalable pipelines for data preprocessing, model training, evaluation, and deployment
- Develop quantitative imaging methods (e.g., volumetrics, density measurements, biomarker extraction)
- Leverage curated, multi-institutional datasets to ensure model generalizability and robustness
- Collaborate with radiologists and engineering teams to define clinically meaningful outputs
- Produce regulatory-grade documentation for datasets, model development, validation, and performance
- Ensure reproducibility and traceability of experiments (data, model, and code versioning)
- Work collaboratively with regulatory and quality experts to support FDA 510(k) and De Novo submissions, including providing technical documentation and validation evidence
- Contribute to software quality and security practices, including supporting activities such as vulnerability assessment and penetration testing in collaboration with cybersecurity and regulatory teams
- Utilize modern AI-assisted development tools (e.g., LLM-based coding agents) to accelerate development and improve code quality
- Participate in team-based development practices (code reviews, Git, testing frameworks)
- Support manuscripts, grants, and technical reporting
Minimum Education and/or Training:
- Bachelor's degree in computer science, data science, information science, business, or related field.
- Master's degree preferred.
Minimum Experience:
- Minimum Requirement: Bachelor's degree with 5+ years of experience required.
- Experience Exception: Master's degree with 3+ years of experience.
- Experience with programming languages, databases, and software development lifecycle
- Experience with the position-specific technical stack preferred
- Experience with the position-specific scientific domain preferred
- Proven performance in earlier role/comparable role
Preferred Qualifications
- 3+ years of experience developing ML/DL models for image analysis
- Demonstrated experience with segmentation, detection, and/or quantitative imaging algorithms (2D and/or 3D)
- Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience with modern architectures (U-Net variants, detection frameworks, transformers, or foundational models)
- Familiarity with DICOM and medical imaging workflows
- Strong understanding of evaluation metrics (Dice, IoU, ROC/AUC, sensitivity/specificity)
- Experience with version control and collaborative development (e.g., Git)
- Demonstrated ability to produce clear, structured technical documentation
- Experience using modern LLM-based coding assistants (e.g., Claude, Codex, or similar) to enhance development workflows
- (Strongly preferred) Experience developing and documenting AI solutions for clinical translation or regulatory submission (e.g., FDA 510(k))
- Familiarity with Good Machine Learning Practice (GMLP)
- Experience collaborating with regulatory, quality, or cybersecurity teams
- Exposure to software security principles (e.g., secure coding, vulnerability assessment, penetration testing concepts)
- Experience with large, multi-institutional datasets
- Familiarity with radiology workflows and quantitative imaging biomarkers
- Experience with cloud or high-performance computing environments
- Experience deploying models into research or clinical environments
Academic and Career Development Opportunities
- Significant opportunities for authorship on high-impact manuscripts
- Active participation in multi-institutional research collaborations
- Opportunities to contribute to grant proposals and funded research initiatives
- Exposure to translational AI development, including projects targeting FDA 510(k) clearance
- Ability to build a strong academic portfolio in parallel with real-world clinical impact
Key Attributes
- Highly collaborative and team-oriented
- Detail-oriented with strong commitment to documentation, reproducibility, and auditability
- Able to operate effectively in a translational, regulatory-aware environment
- Strong interest in delivering clinically impactful AI solutions
Compensation
In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specific salary offer takes into account factors that are considered in making compensation decisions including but not limited to skill sets, experience and training, licensure and certifications, and other business and organizational needs. It is not typical for an individual to be hired at or near the top of the salary range and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current salary range is $104,000 - $186,160 per year for the role of Staff Software Engineer / Machine Learning Engineer - Radiology.
Explore our exceptional benefits!
St. Jude is an Equal Opportunity Employer
No Search Firms
St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude.
What St. Jude Children's Research Hospital employees say
Pay
Benefits
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Workplace
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About St. Jude Children's Research Hospital
Sourced by ZipRecruiter
Industry
Health care and social assistance
Company size
1,001 - 5,000 Employees
Headquarters location
Memphis, TN, US
Year founded
1962