Hey, I'm Noah.

I'm an AI Systems Engineer and ML Engineer studying for an M.Sc. in Artificial Intelligence at UC Berkeley & NTNU. I build production-grade AI systems, multi-agent pipelines, and scalable backend infrastructure.

Noah Lund Syrdal

Currently

  • Finishing M.Sc. coursework at UC Berkeley — NLP, recommender systems, big data, neurotechnology
  • Research on in-context learning robustness, carbon-aware recommendations, and healthcare accessibility modeling
  • Latest vLLM benchmark finding: increasing concurrency from 1 to 8 mainly raised queueing latency, and doubling max_tokens from 128 to 256 roughly doubled latency while tokens/sec stayed similar
  • Stepping into Head of Technology at ReLU NTNU

Experience

AI/Software Engineering Intern

Spacial AI · Palo Alto, CA

2025

IT Consultant Intern

Bouvet · Oslo, Norway

2025

Learning Assistant

NTNU · Trondheim, Norway

Jan–May 2025

Education

Noah and friends at UC Berkeley

UC Berkeley

2025 – Present

Exchange Year · GPA 3.65/4.0

NTNU

2022 – Present

M.Sc. Artificial Intelligence · B.Sc. Informatics (GPA 4.2/5.0)

Awards

DigitalOcean Hackathon

1st Place — DigitalOcean Hackathon, San Francisco

Future of Labor Hackathon

2nd Place — Future of Labor Hackathon, San Francisco

Leadership

Head of Technology — ReLU NTNU

Norway's leading AI student organization. Driving tech choices for projects and keeping the organization at the forefront of AI/ML.

2026 – Present
Datakameratene FK team
Noah with league trophy

CEO — Datakameratene FK

Led the club to a league championship. Finance, sports administration, and team operations.

2023 – 2025

Projects

view all →

LLM Inference Benchmark

Open-source benchmark harness for comparing LLM inference performance. Latest vLLM result: on local single-worker Qwen2-0.5B-Instruct runs, higher concurrency mainly increased queueing latency while tokens/sec remained similar.

PythonLLMs
GitHub →

PR Reviewer

Automated pull request review tool powered by LLMs.

PythonDevTools
GitHub →

Carbon-Aware Recommender System

Studying the Pareto frontier between user engagement and carbon footprint in recommendation systems.

PythonMLRecSys
GitHub →

Skills

AI/ML: LLMs, RAG, agentic systems, NLP, recommender systems, RL (MuZero, MCTS)

Backend & Systems: Python, distributed systems, APIs, caching, observability

Cloud: AWS (Lambda, API Gateway, S3, Bedrock), Docker, GCP

Languages: Python, TypeScript, Java, SQL