Michael Stanley
I'm a data scientist and ML engineer specializing in computer vision and MLOps.
About
I’m a data scientist and ML engineer specializing in computer vision and MLOps. I enjoy turning cutting-edge CV research into production-grade solutions and building end-to-end ML systems, including on-device inference and cloud pipelines. Based in Auckland, New Zealand, I love prototyping AI-native products and collaborating with cross-functional teams to deliver measurable impact.
Over the past few years I’ve designed ML pipelines for image clustering, lifelike fish tracking, and satellite imagery analysis, as well as web-based tools for information extraction using LLMs. I’m comfortable bridging research and production, developing data-labeling tools, and communicating insights clearly to stakeholders.
- 7 years' experience
- Joined January 2026
Skills· 13
Languages
- EnglishFluent
- Spanish; CastilianIntermediate
Industry experience
- Computers & Electronics
- Software & Internet
- Life Sciences
- Professional Services
- Media & Entertainment
Work experience· 4 roles
Designing and building iOS applications powered by large language models. Developing end-to-end LLM solutions, including on-device inference, APIs, and cloud pipelines. Prototyping AI-native products and applied ML systems for real-world deployment.
Designed and implemented advanced machine learning systems to enhance professional photo culling and selection. Built an ML framework for clustering images, created a large language model data pipeline for extracting meaningful text from clustered image sets, and developed a Rust-based data-labeling solution integrating Databricks and AWS imagery. Implemented a high-sensitivity image comparison system to localise subtle differences between image pairs.
Led the engineering and MLOps development of a 13-species fish tracking and length estimation system using video footage captured at sea. Built secure cloud-based ETL pipelines, trained ML algorithms, and produced annotated videos/CSV outputs for fisheries decision-makers. Conducted wetland segmentation with multi-spectral imagery and vegetation species prediction for protected-region monitoring; presented MIGARS 2024.
Education
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