Senior AI/ML Engineer - Bioinformatics & Cloud Pipeliner

Jackey Nakamura

Senior AI/ML Engineer Full-Stack Python, R, Bioinformatics, AI/ML, AWS, GCP

Los Angeles, CA +1-810-***-****

********@*******.***

***********@*****.***

https://www.linkedin.com/in/jack

Professional Summary

Senior AI/ML Engineer with 10+ years of experience in computational biology and bioinformatics, specializing in multi-omics data analysis, AI/ML models, and scalable data pipelines. Deep expertise in designing and implementing computational workflows for complex biological datasets, including RNA-seq, proteomics, and metabolomics. Proven track record in integrating emerging AI techniques with standard bioinformatics approaches to drive scientific discovery in metabolic diseases and other therapeutic areas. Experienced in cloud computing platforms (AWS, GCP) and bioinformatics pipelines for advanced scientific insights and therapeutic development.

Technical Skills

Computational Biology & Bioinformatics

Languages: Python, R, Bash

Bioinformatics Tools: Nextflow, Bioconductor, DESeq2, edgeR, limma, Seurat, Scanpy

Omics Data: RNA-seq, single-cell genomics, proteomics, metabolomics, multi-omics integration

AI/ML Models: Supervised & unsupervised learning, neural networks, ensemble models, RNN, CNN, LSTM

Cloud Platforms: AWS (SageMaker, Lambda, EC2), GCP (BigQuery, Vertex AI), Azure ML

Workflow Management: Nextflow, Apache Airflow, MLflow

Data Visualization & Analysis: matplotlib, seaborn, Plotly, Tableau, Power BI

Version Control & Documentation: Git, GitHub, JIRA, Confluence Professional Experience

Lyra Health

Sr AI/ML Engineer

Mar 2024 – Present Remote / Houston, TX

Designed and deployed bioinformatics pipelines for healthcare data, integrating multi- omics datasets (clinical data, RNA-seq) to enable precision mental health treatments.

Utilized AI/ML models to analyze complex data from healthcare studies, including single-cell genomics and proteomics, contributing to advancements in mental health triage and patient support.

Developed scalable data pipelines using Databricks and Delta Lake, focusing on high- throughput analysis of healthcare datasets and ensuring reproducibility in scientific findings.

Collaborated with clinicians and data scientists to integrate clinical data with biosignals, improving predictive models for patient outcomes.

Anthropic (Contract / Advisor)

Sr AI/ML Engineer – GenAI Systems

Feb 2022 – Feb 2024 Remote

Designed and implemented RAG (retrieval-augmented generation) pipelines for enterprise clients, focusing on integrating multi-modal data (text and images) for healthcare and financial use cases.

Contributed to developing AI/ML models to analyze complex datasets, leveraging tools like LangChain and Qdrant for efficient retrieval and integration of large biological datasets.

Advised on bioinformatics workflows using R and Python, developing scalable data analysis pipelines to enhance insights from multi-omics and clinical datasets. Amazon Web Services (AWS)

Sr AI/ML Engineer

July 2017 – Jan 2022 Remote / Seattle, WA

Led the development of scalable bioinformatics pipelines using AWS services

(SageMaker, Lambda) for processing genomic and proteomic data in real-time.

Designed cloud-native solutions to process large-scale genomic datasets, improving predictive models and accelerating research in metabolic diseases and genomics.

Implemented automated data analysis workflows for high-dimensional datasets, ensuring reproducibility and version control in bioinformatics projects. Education

Southern Methodist University

M.S. in Computer Science, 2015 – 2017 Dallas, TX University of Texas at Austin

B.S. in Statistics, 2009 – 2013 Austin, TX

Key Contributions

Optimized data workflows for multi-omics datasets, integrating clinical, RNA-seq, and proteomics data to enable more accurate patient predictions in mental health applications. This led to a 30% improvement in model performance across clinical support workflows.

Developed a scalable AI/ML pipeline using Databricks and Delta Lake, handling diverse healthcare data to create insights that inform mental health triage decisions, ensuring compliance with healthcare standards like HIPAA.

Collaborated with interdisciplinary teams of clinicians, data scientists, and engineers to build and deploy real-time AI models that improved patient outcomes by reducing response time in critical workflows by 32%.

Mentored junior engineers and provided technical leadership on data pipeline design, ensuring the integration of bioinformatics and healthcare data aligned with clinical goals.

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