Wesley Deklich

Engineering intern on compute infrastructure at Meta. Researching multivector retrieval systems and efficient large-scale ML at SSAIL with Minjia Zhang.

Now

A first-author manuscript on multivector retrieval is under review at NeurIPS.

Adjacent threads: training and inference efficiency, systems for experimentation, and the geometry of representation in retrieval-augmented settings.

Selected experience

MetaSoftware Engineering Intern, Compute Infrastructure

Present

Infrastructure for large-scale computation and experimentation.

AiseraAI / ML Engineer Intern

Summer 2025

Ontology-aware neural search for retrieval-augmented systems; clustering and embedding pipelines; FAISS-style index tuning for semantic search.

Research

SSAIL, University of Illinoiswith Minjia Zhang

Ongoing

Multivector architectures and the systems infrastructure required to operate them reliably at scale. First-author manuscript under review at NeurIPS.

Education

Selected work

Meteorite classificationPyTorch, TensorRT

2023

CNN-based classification with edge-oriented inference for geospatial imagery.

Aegis Notes — Electron, TypeScript

2024

Cross-platform notes with markdown editing and Electron IPC persistence.

Investor sentiment — FastAPI, MongoDB

2023

NLP pipelines and storage for unstructured text and dashboards.

Archive

Earlier roles and side projects. Accurate, but ancillary to the work above.

STEM Enhancement in Earth Science (SEES)Research Intern, UT Austin Center for Space Research

2022

Bayesian classification and pairing guidelines for meteorite catalog data; analysis of 10k+ historical records in Python, TensorFlow, NumPy. Pairing framework based on physical attributes, geography, and orbital history. Related work presented at the AGU Fall Meeting.

RingCentralSoftware Engineering Intern

2022

Cloud communication and video services. Latency-oriented analysis in Python and Tableau, D3 / Tableau dashboards.

Aisera — extended notes

Ontology-driven neural search for retrieval-augmented systems with structured entity hierarchies and dense embeddings. Unsupervised clustering with HAC, UMAP, HDBSCAN to organize enterprise knowledge bases. FAISS ANN indices (IVF-PQ, HNSW) for low-latency search over multi-million-vector corpora.

Investor analysis dashboard — FastAPI, MongoDB, NLP

NLTK and spaCy over investor-facing text; MongoDB schema scaled to hundreds of thousands of rows. Dashboard-facing API tuned for high read volume.

Aegis Notes — Electron, TypeScript, Tailwind, Jotai, TensorFlow.js

Markdown editor with preview; persistence via Electron IPC across platforms. Lightweight tagging and similarity hooks with TensorFlow.js.