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

Meta — Software Engineering Intern, Compute Infrastructure

Present

Infrastructure for large-scale computation and experimentation.

Aisera — AI / 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 Illinois — with 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 classification — PyTorch, 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.

RingCentral — Software 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.