AI Research Engineer specializing in Large Language Model Evaluation, Reinforcement Learning from Human Feedback, Natural Language Processing, Graph Neural Networks, AI Safety, Benchmarking, and Scalable ML Systems.
Structured evaluation workflows for factuality, alignment, coherence, safety scoring, and RLHF evaluation of Large Language Models.
Benchmarking pipelines for GCN, GAT, and GraphSAGE architectures across graph datasets and predictive modeling tasks.
Annotation QA workflows for AI-generated outputs including ranking, relevance scoring, factuality checks, and safety evaluation.
Google | 2023 – Present
GNN research, LLM evaluation, benchmarking, ML pipelines, and AI safety workflows.
Microsoft | 2020 – 2022
LLM/NLP evaluation, graph ML, experimentation workflows, and technical reporting.
TELUS International | 2018 – 2020
RLHF workflows, annotation QA, model output evaluation, and dataset validation.
International Conference on Machine Learning Systems, 2019
Journal of Artificial Intelligence Research, 2020
International Journal of Data Science and Analytics, 2021
Focused on AI research, scalable machine learning systems, LLM benchmarking, RLHF evaluation, graph machine learning, NLP evaluation workflows, and reproducible model evaluation pipelines.
Available for remote AI research, LLM evaluation, RLHF, machine learning,
benchmarking, software engineering, and technical research projects.
Email: ruslandavidenko0706@gmail.com