Computer science · Machine learning systems

Connor M. Carpenter

I build systems for AI.

Computer Science student at Carnegie Mellon University concentrating in Machine Learning, graduating May 2027. Deep Learning Algorithms Engineering Intern on NVIDIA Dynamo, working on inference serving, distributed orchestration, and open-source engine integrations.

Explore the interactive journey The places that made me

01 / Experience

Building across AI, systems, and research.

NVIDIA

Deep Learning Algorithms Engineering Intern, Dynamo

May 2026 - Present Santa Clara, CA
  • Designed Dynamo's sidecar architecture across vLLM, SGLang, and TensorRT-LLM, preserving native engine entrypoints while adding out-of-process routing, lifecycle management, and distributed orchestration.
  • Contributed upstream to the vLLM, SGLang, and TensorRT-LLM open-source projects while co-designing sidecar lifecycle, failure-recovery, multimodal, and KV-cache contracts with each engine team.
  • Created and open-sourced OpenEngine, a vendor-neutral gRPC/Protobuf protocol defining typed APIs for inference, discovery, health, abort, drain, and KV coordination across engines and distributed frameworks.

Persona Machines

Co-Founder & Chief Technology Officer

May 2025 - Feb. 2026 Pittsburgh, PA
  • Engineered the end-to-end DeepCloak MVP from scratch, deploying a responsive SvelteKit frontend on Vercel and a high-concurrency FastAPI backend on Railway backed by Neon (PostgreSQL) and SQLAlchemy.
  • Under the advisement of Professor Ruslan Salakhutdinov (ex-VP of Research, Meta), architected the platform's novel privacy protocols, leveraging this technical differentiation to secure acceptance into the NVIDIA Inception program.

NASA Langley Research Center

Dynamic Systems & Controls Intern

Jun. 2024 - Aug. 2024 Hampton, VA
  • Programmed software in MATLAB for the calculation of Sliced-Normal distributions which characterize complex, multivariate data sets with multiple modes and strong dependencies.
  • Created a library of custom functions for enhancing existing datasets through virtual data augmentation.

The Center of Excellence for Engineering Biology

Software Engineering Intern, Genome Project-write

Jun. 2023 - May 2024 New York, NY
  • Developed and refined core features for the computer-aided design (CAD) platform for genome editing within an agile team, directly supporting an international community of over 200 researchers.
  • Constructed and deployed serverless backend services using Python and AWS Lambda to power the platform's genome synthesis and editing capabilities.

Thomas Jefferson National Accelerator Facility

Research Intern

Jun. 2023 - Aug. 2023 Newport News, VA
  • Processed and analyzed large-scale experimental data from electron-proton collisions, developing scripts in C++ and Python within the ROOT framework to deliver a new estimation of the proton charge radius.
  • Designed and built an educational platform featuring tutorials and code examples to streamline and accelerate the onboarding of new researchers onto the lab's ROOT analysis software.

02 / Selected projects

Systems built from first principles.

01

CMU 15-445

Relational Database Engine (BusTub)

  • Implemented BusTub's storage layer in C++, including a thread-safe buffer pool with ARC replacement and asynchronous disk scheduling, plus a concurrent B+ Tree supporting splits, merges, ordered scans, and tombstone-buffered deletion.
  • Built vectorized SQL executors and rule-based optimizer transformations for joins, aggregation, external merge sort, window functions, and index scans; added optimistic MVCC with undo-log version chains and snapshot/serializable isolation.

C++, DBMS Internals, Concurrency

02

CMU 15-213

Dynamic Memory Allocator

  • Implemented a 64-bit C allocator with malloc, free, realloc, aligned blocks, and heap-consistency validation.
  • Built segregated free lists with block splitting and immediate coalescing, achieving a 100.0 performance index, 8,136 Kops throughput, and 74.1% memory utilization.

C, Systems Programming, GDB

03 / Education & skills

Theory grounded in building.

Education

Carnegie Mellon University, School of Computer Science

Expected May 2027

Bachelor of Science in Computer Science; Concentration in Machine Learning

Pittsburgh, PA · GPA 3.88/4.00

Relevant coursework

Deep Learning SystemsMachine Learning with Large DatasetsDeep Reinforcement Learning & Control (Fall 2026)Database SystemsParallel and Sequential Data Structures and AlgorithmsNeuro-Symbolic AI

Languages

  • Python
  • C
  • C++
  • Rust
  • TypeScript
  • SQL
  • Java
  • SML
  • MATLAB
  • R
  • HTML/CSS

AI & Inference

  • Inference Serving
  • PyTorch
  • Dynamo
  • OpenEngine
  • vLLM
  • SGLang
  • TensorRT-LLM
  • gRPC
  • Protobuf

Systems & Data

  • Distributed Systems
  • Fault Tolerance
  • PostgreSQL
  • SQLAlchemy
  • asyncio
  • Concurrency
  • DBMS Internals

Web & Cloud

  • SvelteKit
  • FastAPI
  • REST
  • OpenAPI
  • API Design
  • React
  • Node.js
  • AWS Lambda
  • Docker
  • Kubernetes

Developer Tools

  • Linux
  • Git
  • GDB
  • GTest
  • Vim
  • CI/CD

Spoken Languages

  • English
  • Spanish (Conversational)
  • French (Conversational)