Dennis K.
AI Linguistic Annotation Specialist
Experience
Jul 2025 - Jul 2025
1 monthAI Linguistic Annotation Specialist
Argos Multilingual
- Verified and re-annotated 264,000 words of English literary text to ensure accurate character-dialogue identification, boundary precision, and consistent tag alignment
- Reviewed and corrected AI-generated dialogue spans, ensuring quotation marks and speech boundaries were properly enclosed within Character tags, improving overall dialogue-span correctness by 35%
- Cross-referenced and expanded the Character List, updating aliases, gender, and descriptive metadata to maintain canonical consistency and eliminate false associations
- Distinguished spoken dialogue, impersonations, and group speech from narration or inner monologue, reinforcing data reliability for downstream NLP model training
- Conducted context-aware analysis by referencing prior and subsequent chapters to accurately resolve ambiguous or implied speakers
- Applied rigorous QA checklists to validate tag completeness, character accuracy, and annotation coverage in line with the Character Dialogue Annotation Guidelines
- Leveraged Argos annotation portal tools (tag-pair editing, alias lookup, and live metadata updates) to streamline workflow and boost annotation throughput
- Collaborated with the project manager to integrate feedback loops that maintained 99%+ labeling precision and rubric compliance across all submissions
- Delivered all annotations on time, demonstrating exceptional accuracy, quality assurance discipline, and time-management capability
- Recognized by Argos project leads for exemplary data quality, contextual comprehension, and contribution to the project's award-level evaluation benchmarks
Jul 2024 - Present
1 year 5 monthsLondon, United Kingdom
AI Data Specialist
GoodNotes
- Designed structured annotation frameworks that improved recommendation precision by 25% in content discovery models
- Led end-to-end data labeling, content evaluation, and model behavior testing pipelines supporting AI-powered note-taking and productivity systems
- Designed and executed LLM evaluation experiments to assess reasoning accuracy, factual grounding, summarization quality, and multimodal coherence
- Developed scalable annotation workflows integrating automated validation checks and reviewer calibration systems, increasing evaluation throughput by 30%
- Conducted A/B testing and regression analyses across projects to detect behavioral drift and refine generative model performance
- Created rubric-based scoring frameworks and QA templates to measure prompt faithfulness, coverage, and content safety across text, image, and audio tasks
- Spearheaded cross-platform evaluation initiatives to standardize annotation logic and ensure inter-build consistency
- Partnered with research and engineering teams to apply prompt refinements and cross-build testing, driving a 25% improvement in model response accuracy
- Authored structured evaluation reports summarizing quality trends, scoring distributions, and insights that inform fine-tuning and policy updates
- Mentored and trained cross-functional reviewers to maintain rubric fidelity, ensuring uniform QA practices across multimodal and multilingual datasets
- Maintained 99%+ task completion adherence with record-high accuracy and precision in all evaluation cycles
Jun 2024 - Feb 2025
9 monthsBoca Raton, United States
Data Annotation Specialist
e2f, inc.
- Evaluated LLM-generated responses across five quality dimensions, ensuring dataset consistency for training
- Evaluated and annotated multimodal AI data across text, image, and video dimensions using quality metrics such as Helpfulness, Relevance, Completeness, and User Utility to improve discovery and reasoning accuracy
- Conducted preference ranking, interactive dialogue evaluations, and retrieval-augmented response verification to assess factual consistency and model grounding
- Designed and applied persona-based evaluation prompts and multi-turn reasoning scenarios to strengthen contextual alignment and user experience in conversational AI systems
- Implemented structured scoring frameworks and QA guidelines to ensure classification accuracy, data consistency, and alignment with project-specific benchmarks
- Performed spatial-temporal and entity recognition analyses in video-based annotation workflows, ensuring precision in reasoning, event sequencing, and contextual understanding
- Executed content correctness, citation verification, and claim-level accuracy reviews, identifying hallucinations and reasoning gaps in LLM outputs
- Collaborated with cross-functional QA teams to enhance annotation standardization, achieving 20% improvement in inter-annotator agreement and sustaining 99%+ audit precision
- Consistently exceeded throughput and quality targets, contributing to large-scale data readiness for next-generation model fine-tuning and behavior evaluation
May 2024 - May 2024
1 monthSan Mateo, United States
LLM Data Trainer
SuperAnnotate
- Utilized the SuperAnnotate platform to design structured prompts and responses for LLM behavior training across diverse reasoning and summarization tasks
- Trained and evaluated content classification models using sourced and curated data to enhance factuality, helpfulness, and user relevance
- Created and refined multi-turn conversational flows, improving dialogue naturalness and persona coherence for interactive AI assistants
- Authored internal annotation and prompt-design standards, ensuring consistency in tone, response formatting, and reasoning fidelity
- Collaborated cross-functionally to enhance data quality pipelines, achieving measurable gains in model consistency and fine-tuning readiness
Feb 2022 - May 2022
4 monthsBern, Switzerland
Medical Data Annotator
RetinAI Medical
- Annotated and validated large-scale medical datasets for computer vision models supporting ophthalmology and diagnostic AI research
- Classified clinical imaging and textual data with precision, achieving high inter-annotator reliability across complex medical attributes
- Validated content classification outputs against medical accuracy benchmarks, ensuring compliance with healthcare data standards
- Identified critical diagnostic trends within datasets to support model retraining, improving performance in early disease detection
- Contributed to model pipeline readiness, ensuring datasets met regulatory and quality thresholds for integration into healthcare workflows
Feb 2021 - Nov 2023
2 years 10 monthsData Labeler (Internal Reviewer)
Focal Systems
- Enhanced retail automation AI systems by training computer vision models on over 2 million labeled images covering products, compliance patterns, and inventory data
- Conducted high-precision QA and internal review cycles, improving accuracy for deep learning models used in retail digitization
- Designed annotation validation workflows that reduced classification turnaround time by 25% while maintaining 98%+ audit accuracy
- Improved product detection pipelines by refining object localization and visual classification standards, supporting scalable commercial deployments
- Collaborated with data scientists and engineers to establish feedback-driven retraining loops, driving sustained model accuracy improvements
Aug 2020 - Feb 2021
7 monthsCape Town, South Africa
Data Labeler
Enlabeler
- Labeled and classified large-scale multilingual datasets for machine learning models across visual and text-based content domains
- Implemented QA and verification standards to ensure balanced dataset representation and model fairness across content types
- Streamlined internal labeling workflows, reducing redundancy and improving annotation throughput by 20%
- Collaborated with global data teams to align classification outputs with client-specific taxonomies and domain ontologies
- Recognized for precision and consistency in early-stage AI model development across diverse labeling projects
Jun 2020 - Jun 2021
1 year 1 monthPalo Alto, United States
Map Data Annotator
Gatik
- Annotated high volumes of geospatial and navigation imagery to support autonomous vehicle perception models
- Delivered precise labeling of road features, lane markings, and environmental cues, boosting model navigation reliability
- Applied QA protocols and multi-tier accuracy reviews, achieving consistent alignment with safety-critical annotation benchmarks
- Collaborated with machine learning engineers to improve map data validation frameworks, enhancing the efficiency of training datasets
- Maintained top-tier productivity and accuracy metrics, leading to additional project assignments within the annotation pipeline
Jan 2020 - Mar 2020
3 monthsGdańsk, Poland
Data Annotation Specialist (NLP)
Tagtog
- Annotated large volumes of natural language text to create structured datasets for NLP and semantic search models
- Enhanced content categorization accuracy through detailed entity recognition, relation mapping, and linguistic context tagging
- Contributed to model refinement and retraining cycles, ensuring dataset diversity and alignment with language model benchmarks
- Built an extensive, high-quality text corpus that accelerated NLP model tuning and improved classification and search relevance
- Collaborated cross-functionally with linguists and data scientists to optimize annotation workflows and ensure consistent project delivery
Jul 2019 - Nov 2019
5 monthsHasselt, Belgium
Data Annotator
Humainly
- Labeled and classified high-volume visual datasets to train AI models for content recognition, discovery, and recommendation systems
- Improved annotation efficiency and throughput by refining labeling workflows and establishing consistency across multiple task categories
- Ensured top-tier data integrity by applying rigorous QA checks, increasing model performance accuracy in production environments
- Contributed to the scaling of annotation operations by optimizing task segmentation and automation readiness
- Maintained project deadlines with exceptional accuracy, earning recognition for reliability and consistency across multi-region teams
Jun 2018 - Nov 2020
2 years 6 monthsNairobi, Kenya
Data Entry Specialist
CloudFactory
- Supported Just Appraised's AI-driven property and document automation workflows, annotating and validating data used to enhance record digitization for county assessors and real estate documentation
- Processed and reviewed land deeds, property transfers, permits, and indexing data, improving document recognition accuracy and classification precision by 28% through refined metadata tagging
- Leveraged Hudl and in-house annotation tools to classify structured and unstructured data, ensuring compliance with Just Appraised's domain-specific taxonomies and legal indexing requirements
- Identified and corrected metadata inconsistencies across thousands of property records, contributing to a 35% reduction in downstream error propagation during AI model training and validation
- Collaborated with data engineers and QA reviewers to streamline validation workflows, achieving 99%+ labeling accuracy and meeting weekly throughput targets for bulk document ingestion
- Trained new annotators on document classification standards, entity linking, and OCR-based validation protocols, improving cross-team quality alignment by 25%
- Delivered high-quality annotation outputs under tight turnaround times for local government automation projects supporting over 300 U.S. county clients
- Recognized by CloudFactory project leads for excellence in data integrity, turnaround efficiency, and proactive error detection within Just Appraised's automation pipelines
Summary
Results-driven AI Data & QA Specialist with demonstrated expertise in LLM behavior testing, multimodal data annotation, and evaluation of AI model outputs across education, productivity, and consumer technology domains. Proven success in designing scalable annotation systems, applying robust evaluation frameworks, and ensuring 98%+ accuracy across complex multimodal datasets.
Recognized for precision and cross-functional collaboration in improving model reasoning fidelity, content safety, and task compliance. Adept at taxonomy design, cross-build consistency evaluation, rubric-driven scoring, and prompt engineering to assess and refine generative model performance. Skilled in leveraging structured QA processes, automation tools, and experimental analysis to deliver measurable improvements in data quality and model performance.
Languages
English
NativeSwahili
AdvancedEducation
Oct 2013 - Jun 2017
Rongo University
Bachelor of Arts · Sociology, Criminology, and Community Development · Kenya · Second Upper Division Honors
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