Open to opportunities

Samuel Renteria

@samuelrenteria

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Machine learning engineer specializing in graph neural networks and production ML systems.

Switzerland

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What I'm looking for

I seek roles building production ML systems and GNN/NLP models where I can optimize inference, scale training, and deliver measurable product impact in collaborative teams.

I am also deeply interested in creating complex systems with robust safeguards.

I am a machine learning engineer focused on graph neural networks, NLP, and production ML deployment, with hands-on experience building efficient GAT architectures and end-to-end inference systems. I design scalable training pipelines, optimize models for latency and memory, and deploy services handling thousands of daily requests with high uptime.

My work includes implementing distributed PyTorch DDP training, INT8 quantization, TorchScript compilation, and automated hyperparameter search with Ray Tune, alongside data engineering and web development projects that reduced no-shows, completed large migrations, and delivered analytics-driven product improvements.

Experience

Work history, roles, and key accomplishments

Current

Python Developer

Current

NDA

Jul 2024 - Present (1 year 4 months)

Developed a custom 3-layer GAT for multilingual knowledge integration achieving 92% F1 (vs. 77% BERT baseline) and reduced model footprint 60%; built production FastAPI inference system serving 5K+ daily requests with p95=85ms and 99.8% uptime.

Python PyTorch fastAPI Redis Model Quantization TorchScript MLFlow Distributed Training

Software Developer

Private ENT Clinic

May 2020 - Aug 2021 (1 year 3 months)

Implemented HIPAA-compliant Twilio SMS reminder system reducing no-shows by 35% and recovered ~$4,200/month; built ETL migration for 5,000+ patient records with 99.8% accuracy and delivered in 6 weeks.

Python Pandas SQLAlchemy PostgreSQL Twilio React ETL Data Validation Docker

Education

Degrees, certifications, and relevant coursework

University of Lausanne (UNIL)

Sciences du langage et de l'information (Cognitive Science)

2021 - 2024

Activities and societies: Technical projects: PyTorch Geometric graph models, LLM fine-tuning (LoRA), neuro-symbolic reasoning, ETL pipelines processing large text corpora.

Completed studies in cognitive science with coursework and projects in machine learning, graph neural networks, and data engineering, including implementation of graph language models and LLM fine-tuning pipelines.