Open to opportunities

Cody Chapman

@codychapman

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Machine Learning engineer turning data into production AI impact.

United States

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What I'm looking for

I seek a full-time role in generative AI, computer vision, or autonomous systems with strong engineering practices, opportunities to deploy production models, and collaborative teams focused on measurable business impact.

I am a results-driven Data Scientist and Machine Learning Engineer with experience building production AI systems at Fortune-500 companies and research labs. I specialize in deep learning, large-scale data pipelines, and MLOps, and I deliver measurable business value.

At Fidelity I developed a hybrid XGBoost + LSTM fraud model that reduced false positives by 42% and produced $2.3M in annual savings, and I engineered Spark + AWS Glue pipelines processing 10+ TB of daily transaction data. I also designed a company-wide A/B testing platform adopted by multiple business units.

In research and internships I have fine-tuned multilingual transformer models achieving high cross-language accuracy, led graph neural network work (NeurIPS 2025 paper accepted), and authored internal best-practice guides. I mentor others, secure research funding, and build dashboards and tools adopted by hundreds of analysts.

I seek full-time roles in generative AI, computer vision, and autonomous systems where I can apply my strengths in model development, deployment, and scalable data infrastructure to solve high-impact problems.

Experience

Work history, roles, and key accomplishments

Current

Undergraduate Research Assistant

Current

Northeastern AI for Society Lab

Sep 2023 - Present (2 years 9 months)

Led graph neural network research on misinformation spread (NeurIPS 2025 paper accepted), mentored six undergraduates on MLOps best practices, and secured a $15,000 university grant for GPU resources.

MLOps PyTorch Research Mentoring Grant writing GPU Clusters

Machine Learning Engineer

Fidelity Investments

Jan 2025 - Jun 2025 (5 months)

Built a hybrid XGBoost+LSTM fraud detection model that reduced false positives by 42% and enabled $2.3M annual savings; engineered Spark and AWS Glue pipelines processing 10+ TB daily and designed an A/B testing platform adopted by five business units.

XGBoost PyTorch Spark AWS Glue Airflow SQL A B Testing MLOps

Data Science Intern

IBM Watson AI Lab

Jun 2024 - Dec 2024 (6 months)

Fine-tuned multilingual BERT/RoBERTa models achieving 91% accuracy across eight languages and developed a real-time Streamlit dashboard used by 200+ analysts; authored an internal efficient LLM fine-tuning guide.