Selene Gisela
portfolio · est. 2026

Hi, I'm Selene Gisela, I turn curiosity into research and research into things that work.

Engineer turned ML systems builder. I spent years designing physical car parts before falling for language models, and now I build agentic systems that pull structure out of noisy real world text. Currently fascinated by how far you can push an LLM before it needs a human.

about

I started out in mechatronics, designing and releasing physical car parts at Stellantis. Somewhere in there I got stuck on a question I couldn't let go of: is a control model with feedback really any different from a machine learning model? Both take in signals, correct themselves, and get better at hitting a target. Chasing that question is how I ended up in AI.

It led me to a master's in Data Science and Computational Linguistics at UBC, where the thing that hooked me turned out to be language: the messiest, most ambiguous signal of all. These days I am exploring and learning about LLM powered systems and agentic pipelines: the kind of work where you take noisy, real world text and coax something structured and reliable out of it.

I love problems that sit between disciplines, where engineering discipline meets human intent, and where the hard part isn't the model but figuring out what "correct" even means. I like making systems that are honest about what they know, and useful because of it.

Based in
Vancouver, BC
Focus
NLP, Supervised Learning, Unsupervised Learning, Data Engineering
Portrait of Selene Gisela
experience

Where I've worked

Apr 2026 — Jul 2026

Jeppesen ForeFlight

NLP Engineer — Capstone Project

  • Built and shipped an end to end LLM extraction pipeline that turns noisy transcripts into 11 structured fields, replacing a manual reporting process, covering data preparation, prompt engineering, evaluation, and productionization.
  • Built the data foundation: designed ETL pipelines on Databricks following Medallion Architecture (Bronze, Silver, Gold), ingesting raw data from three sources into Bronze, applying quality filters, normalization, and deduplication into Silver, and merge upserting curated results into Gold Delta tables (PySpark, Spark SQL, Delta Lake).
  • The extraction engine is a three layer extraction cascade regex → dictionary lookup → LLM fallback that parses structured emergency event fields from noisy ASR/OCR/subtitle ATC transcripts in a Databricks pipeline
  • Designed a LLM agent with three stages(Spotter, Classifier, Linker) with prompt chaining for entity recognition, role assignment, and relationship extraction.
  • Deployed it as a FastAPI REST service on Databricks Apps against a live LLM serving endpoint, owning architecture through production.
  • Added RAG grounding plus validation against a 5,000+ record reference dataset to cut hallucinations and catch bad output before release.
May 2022 — Aug 2025

Stellantis

Design and Release Engineer - Product Engineer

  • Design, test, validate and release parts for assigned vehicle families; analyze and solve fails produced in parts. Release changes for assigned vehicle groups. Request, oversee and authorize testing.
  • Join forces in Steering Wheel and Driver Air Bag for Restraints team. Manage 100% of emblem design with a new lighting challenge unlike any existing on the industry and development for DODGE SRT programs.
  • Collaborated on 5 cross-functional teams and 10 suppliers through a new vehicle program, translating rough requirements into technical specs and support program timing.
  • Monitor production process and reviewed the requirements, standards, design assurance plan and design practices were 100% met at every step of each process.
  • Analyzed test and warranty data for root causes and drove fixes through formal change control, closing change notices 20% ahead of schedule.
  • Carry out common change documents (CCD), product engineering requests (PER), interim authorization approvals (IAA) and TSAs. Follow up with tooling kick off (TKO) process. Direct developments processes including GD&T validation, DFMEA, PFMEA, DVP&R. Collaborate with design and analysis of 3D math models. Solve warranty complaints and GIMs. Oversee build events such as prototypes, first off tool and PPAP.
  • Wrote formal engineering documentation and validation plans where precise, unambiguous wording carried contractual and safety weight.
  • Value Optimization for all Restraints components (steering wheel, driver air bag and seta belt) and all vehicle programs.
May 2019

Beautiful Patterns (MIT and Tec de Monterrey)

Teaching Assistant — Volunteer

  • Supported computational thinking exercises (HTML, C++, Python) in a program for women in STEM.
selected projects
data engineeringNLP

Capstone Project

End to end LLM extraction pipeline for automated reporting.

View project →
researchpytorch

Membership Inference Attack

Privacy attacks that detect whether specific samples were in a model's training data, including a low-confidence-token method that was the strongest single attack on a fine-tuned LLM plus contrastive-margin attacks for the multimodal setting.

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RAGdata engineeringnlp

RAG Cookbook

End-to-end retrieval-augmented generation system that parses a cookbook into structured data, embeds it for semantic search, and answers grounded questions with LLM routing and a recipe scaling action.

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nlplow resource

AI Foreshadowing

1M word annotated corpus with BiLSTM and LR models via transfer, multitask, and few-shot methods for detecting narrative foreshadowing, deployed as a Dockerized FastAPI + Streamlit app.

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classificationpytorch

Rating Predictions

5 class sentiment classifier for 35K Yelp reviews that benchmarks a TF-IDF + Logistic Regression baseline against a tuned PyTorch neural model, packaged as a tested, modular pipeline with a CLI.

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classification

Default Prediction

Gradient-boosted classifier predicting credit card payment default from account and repayment history features.

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classificationnlp

NLI Classifier

NLP pipeline that classifies English learners' native-language family from their writing, reaching a 9-point lift over baseline, using three interpretable feature families and a grid-searched decision tree.

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toolbox
NLPSupervised LearningChange ManagementDeep LearningUnsupervised LearningContext EngineeringA&DSResearchData VisualizationData EngineeringDashboardsCorpus LinguisticsMachine TranslationComputational SemanticsComputational MorphologyPythonRC++CommunicationDatabricksPySpark
paper notes

Notes, highlighted

Notes on papers I'm reading or topics of my interest; what they claim, what convinced me, what didn't. Written for future me, shared in case it's useful to you.

№ 003 2026-06-24 topic · Machine drafts, controller decides

Aero corpus, turning raw ATC radio into structured incident records

Fast enough to draft an emergency notice in seconds, built so a person always makes the final call. What does it take to read air traffic radio the way a system can actually act on it?

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№ 001 2026-04-22 topic · Low resource languages

Zapotec as a low resource language in Machine Learning

What is a low resource language, and what does Zapotec, one of Mesoamerica's oldest writing traditions, teach us about the limits of NLP?

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№ 002 2026-04-12 topic · Transactions on Affecting Computing

Empathy in technology

On: "Empathy by Design: The Influence of Trembling AI Voices on Prosocial Behavior", Fotis Efthymiou and Christian Hildebrand, 2024

Notes on how trembling AI voices shape empathy, perception, and prosocial behavior in conversational AI.

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№ 004 2026-03-14 topic · Machine Learning RAG

Need a new recipe for today?

Meet Betty Talker. she knows exactly one cookbook by heart, will resize any recipe on request, and flatly refuses to invent a recipe for edible airplanes (yes, that was a real test case).

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№ 004 2026-01-31 topic · Words in, starts out

How many stars can a model count?

Teaching a model to read reviews and count the stars.

Read note →