Sama Al-Oda

I build machine learning systems at the intersection of software infrastructure, biomedical applications, and iOS edge intelligence.

McMaster University · Bachelor of Software Engineering, Double Major with Biomedical Engineering · GPA 3.96 · May 2028

Experience

Machine Learning Engineering Intern (CyberDome / R&D)

Nokia · Ottawa, ON

PythonKubernetesDockerHelmJenkinsGrafana
  • Developed ML-driven anomaly detection services for Nokia NetGuard Endpoint Detection and Response (NEDR), analyzing endpoint and network telemetry to support enterprise-scale threat detection.
  • Built and deployed containerized ML microservices using Kubernetes, Docker, Podman, and Helm.
  • Implemented Jenkins and GitLab CI/CD workflows for automated testing, validation, and deployment while developing Grafana dashboards monitoring 100,000+ endpoints.

Machine Learning & Software Developer Intern

Didar Lab / FendX Technologies · Hamilton, ON

PyTorchTensorFlowSwiftAWSPostgreSQL
  • Architected scalable cloud-based ML applications utilizing PyTorch and TensorFlow, engineering robust inference services through AWS-backed REST APIs (sub-200ms latency).
  • Fine-tuned transformer-based and computer vision architectures using Hugging Face.
  • Engineered cross-platform mobile apps for iOS and Android using Swift and Gradle, integrating CreateML, PostgreSQL, and Firebase.
  • Designed automated computer vision detection pipelines for high-throughput clinical analysis.

Projects

VALID: Multimodal Predictive AI

VALID: Multimodal Predictive AI

CUCAI Responsible AI Award
BERTXGBoostFastAPI

Won the Responsible AI Award at CUCAI 2026 for leading a team of 10 to develop a multimodal ML framework for advanced decision support, achieving 91% precision and 88% macro-recall on 120,000+ records. Architected a high-performance NLP pipeline using a BERT-based transformer deployed via a FastAPI gateway.

X-rAI: Computer Vision Fracture Platform

X-rAI: Computer Vision Fracture Platform

TensorFlowReact.jsFlaskAWS

Developed a TensorFlow-based computer vision model trained on large-scale image datasets, deployed via AWS S3. Built the React.js frontend and Flask backend featuring an integrated LLaMA-powered chatbot for automated report generation.

Disaster Detection App

Disaster Detection App

MEC 2nd Place
SwiftiOSREST APIsTwilio

Developed an iOS application leveraging on-device sensors and a disaster dataset API to detect hazardous events and trigger emergency alerts in real time with < 5s detection latency. Integrated Twilio SMS for instant offline notifications.

Skills

Machine Learning

PyTorchTensorFlowHugging FaceBERTOpenCVCreateML

Infrastructure

KubernetesDockerHelmJenkinsAWSGrafana

Software

PythonSwiftFastAPIReact.jsPostgreSQL