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isaiah amwoma

@isaiahamwoma

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Innovative Artificial Intelligence Specialist with a Ph.D. from Stanford.

United States

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What I'm looking for

I seek a role that values innovation, ethical AI practices, and collaborative growth.

I am an Artificial Intelligence Specialist with a Ph.D. in Computer Science from Stanford University, where I focused on human-in-the-loop machine learning and edge-case training scenarios. With over 7 years of experience in building and supervising AI models, I have a strong foundation in research and applied AI, particularly in remote environments. My work has involved developing scalable training mechanisms and collaborating with diverse teams to enhance AI systems.

In my current role as a Senior Remote AI Training Engineer, I lead the development of remote annotation platforms that engage over 10,000 global contributors. My efforts in designing AI-human hybrid training loops have significantly reduced bias in multi-lingual NLP models. I am passionate about mentoring teams and aligning human feedback with machine learning objectives, which has led to a notable reduction in label noise. I am eager to contribute my expertise to organizations that prioritize intelligent training pipelines and ethical AI development.

Experience

Work history, roles, and key accomplishments

Current

Senior Remote AI Training Engineer

Current

Remote AI Systems, Inc.

Jan 2022 - Present (3 years 5 months)

Led the development and deployment of remote annotation platforms used by over 10,000 global contributors. Designed real-time AI-human hybrid training loops to reduce outlier bias in multi-lingual NLP models and developed quality assurance tools to detect training drift. Mentored distributed annotation teams, reducing label noise by 38%, and integrated dynamic learning systems for human-assisted r

Remote Annotation Systems NLP Quality Assurance Distributed Systems PyTorch TensorFlow Python

AI Researcher & Training Consultant

Freelance

Mar 2020 - Jan 2022 (1 year 10 months)

Partnered with emerging AI startups and educational platforms to build efficient and scalable AI training interfaces. Architected reinforcement feedback systems for intelligent tutoring platforms and conducted international workshops on ethical AI data training and bias mitigation. Designed annotation workflows for autonomous systems, improving domain generalization by 27%.

AI Training Robotics NLP Consulting Python

AI Annotation Workflow Engineer

Google AI

May 2017 - Aug 2018 (1 year 3 months)

Developed intelligent agent-guided feedback systems to optimize annotation team throughput. Piloted a real-time error correction mechanism for labeling teams working on conversational AI datasets. Built a prototype of feedback loops that ranked annotation quality using NLP sentiment of labeler comments.

Annotating NLP Data Labeling Prototyping Python

Education

Degrees, certifications, and relevant coursework

Stanford University

Master of Science in Computer Science, Machine Learning & Distributed Systems

Specialized in large-scale neural network training and low-resource learning environments. Developed modular AI training interfaces for asynchronous contributors in remote research collaborations. Published 2 IEEE papers on distributed fine-tuning models for underrepresented data contexts.

Stanford University

Bachelor of Science in Computer Science, Human-Computer Interaction & Software Systems

Grade: Graduated top 5% of class with Honors in Research

Graduated top 5% of class with Honors in Research. Completed a Capstone Project on Remote Intelligence: A Web Platform for Real-Time AI Supervision and Feedback Integration. Gained practical experience through an internship at Google AI focusing on automated labeling pipelines and feedback clustering from non-technical human reviewers.

Stanford University

Ph.D. in Computer Science, Artificial Intelligence & Human-Centric Learning Systems

Activities and societies: Collaborated with Stanford AI Lab to build human-aware training pipelines for neural networks with natural language reasoning capabilities.

Dissertation focused on Adaptive Training of AI Models in Remote Human-Machine Feedback Loops. Developed scalable, privacy-respecting feedback mechanisms for training models using minimal supervision. Led research in reinforcement learning with sparse, noisy, and outlier feedback datasets across multi-national crowdsourced workers.