Martin C.

Key Skills

Software

Data Annotation Tech

Mercor

Mindrift

Remotasks

Scale AI

Snorkel AI

Top Subject Matter

Photonic-electronic AI accelerators and neural network training

Natural language understanding

entity resolution

Top Data Types

Text

Image

Computer Code Programming

Top Task Types

Fine-tuning

Classification

Point/Key Point

Entity (NER) Classification

Segmentation

Cuboid

Polygon

Bounding Box

Object Detection

Question Answering

Function Calling

Data Collection

Red Teaming

Computer Programming/Coding

Evaluation/Rating

Prompt + Response Writing (SFT)

Text Summarization

Polyline

Text Generation

Transcription

RLHF

Freelancer Overview

Machine Learning Scientist at Lightmatter (September 2018 - November 2020). Brings 19+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Philosophy, Harvard University (2016) and Master of Philosophy, University of Cambridge (2011). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Fine-tuning and Classification.

Labeling Experience

Machine Learning Scientist at Lightmatter (January 2021 - March 2022)

Fine-tuning

Contributed to machine learning science work in a research/industry setting aimed at photonics and AI acceleration. The resume indicates ML responsibilities but does not specify explicit labeling, annotation, or tutoring activities. Overall, the role appears training-focused via ML development rather than manual data labeling. • Performed ML development activities supporting training workflows. • Worked on model performance improvements relevant to photonic ML. • Applied research-driven ML methods in a production context. • Collaborated with teams on AI systems engineering.

2021 - 2022

Machine Learning Scientist at Lightmatter (September 2018 - November 2020)

Fine-tuning

Worked as a Machine Learning Scientist developing techniques to improve low-precision neural network inference and training. Contributed to algorithmic advances that would support model training workflows. Supported deployment-ready training improvements for hybrid photonic-electronic matrix processors. • Developed improved low-precision training/inference approaches. • Contributed to patentable algorithm and hardware architecture work. • Focused on training-time techniques rather than data labeling. • Collaborated on AI accelerator-related model performance improvements.

2018 - 2020

Machine Learning Scientist at Gamalon, Inc. (October 2015 - August 2018)

Classification

Developed machine learning systems including recurrent neural networks and hierarchical Bayesian nonparametric models used for entity resolution and product description parsing. The work supported automated understanding pipelines that rely on supervised training objectives. Data labeling is not explicitly described, but the role is clearly focused on ML model training and deployment for NLP tasks. • Built and deployed ML models for entity resolution. • Implemented hierarchical Bayesian nonparametric approaches. • Applied recurrent neural networks to NLP parsing tasks. • Supported natural language understanding use cases.

2015 - 2018

Education

Work History