Mark W.

Key Skills

Software

No software listed

Top Subject Matter

Document content tagging for NLP topic categorization

Geospatial/GPS event classification data labeling and QA

NLP CRM intent/entity/sentiment labeling and training data quality for large-scale event data

Top Data Types

Text

Image

Document

Top Task Types

Classification

Freelancer Overview

Junior Software Engineer — AI Features (Adobe Systems). Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, Stanford University (2019) and Bachelor of Science, Stanford University (2017). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) and Classification.

Labeling Experience

Senior Software Engineer — ML Platforms (Salesforce, Inc.)

Text

Defined data labeling specifications and annotation schemas for 3 NLP-powered CRM features (intent detection, entity extraction, and sentiment scoring) that feed model training pipelines processing 50M+ daily events. Partnered with data science teams to audit 200K+ labeled samples per quarter using Cohen’s Kappa to measure inter-annotator consistency and flag ambiguous label categories. Authored annotation guidelines and edge-case decision trees adopted as team standard, reducing label disagreement by 34%. • Specified annotation schemas for intent, entities, and sentiment • Measured inter-annotator agreement with Cohen’s Kappa • Authored edge-case guidelines and decision trees • Validated/cleaned datasets prior to training ingestion

2023 - Present

Software Engineer — Data & ML Infrastructure (Lyft, Inc.)

TextClassification

Designed labeling taxonomies for ride-event classification models by specifying 22 label classes and creating annotator-facing style guides. Reviewed and QA-sampled 50K+ annotated GPS trace records per sprint to identify systematic labeling errors and provide structured feedback to annotation vendors. Built automated Python (pandas) validation scripts to detect label drift, class imbalance, and formatting inconsistencies across annotation batches. • Defined 22-class labeling taxonomy • QA sampled 50K+ GPS trace records per sprint • Detected label drift and class imbalance • Documented failure patterns to inform retraining data selection

2021 - 2023

Junior Software Engineer — AI Features (Adobe Systems)

Text

Provided annotation support for Adobe Experience Manager’s content-tagging AI by labeling 30K+ document samples across 15 topic categories and performing spot-check QA on contractor batches. Authored Python preprocessing scripts to normalize and de-duplicate raw text corpora ahead of labeling to reduce annotation rework. Created annotation workflows and decision flowcharts for ambiguous cases to improve annotator onboarding and consistency. • Labeled documents for topic/category tagging • Performed spot-check quality assurance on contractor outputs • Standardized ambiguous-case decision flowcharts • Reduced rework through text normalization and de-duplication

2019 - 2021

Education

Work History