Computer Science @ Cornell (May 2027) · Applied AI/ML @ Microsoft Azure · Distributed Systems @ Apple Pay · NYC
I want to build reliable backend and distributed systems at scale. I'm also interested in AI and cloud infrastructure, as well as applied AI and reinforcement learning.
I'm looking for Summer 2027 new-grad software engineering roles in backend, distributed systems, or AI/cloud infrastructure, preferably in NYC, the San Francisco Bay Area, or Seattle.
Portfolio · LinkedIn · Résumé · rrs244@cornell.edu
- Backend and distributed systems — I love designing reliable systems that stay correct under scale, concurrency, and failure. This was my day to day at Apple Pay, during which I learned how to architect solutions grounded in reliability, privacy, and security at tremendous data volume -- 2500 TPS from Apple Cash. I've also delved into the ETL space at Capital One, and leveraged cloud technologies (AWS/Azure) at both internships to productionize my software.
- AI and cloud infrastructure — I enjoy building the platforms and tooling that make intelligent systems dependable in production. This is an amazing blend of my distributed systems interests above, and Applied AI below, and I'd love to explore this domain further -- how can we not just solve real world problems with AI but more importantly, scale them?
- Applied AI and reinforcement learning — I like turning research ideas into systems that learn, adapt, and solve real problems. Some challenging problems I've explored -- at Microsoft Azure, how do you take a massive incident pipeline of >2B incidences a year from every Fortune 500, and create a relevance sieve for the CPU performance team? And at HealthTrends.AI, how do you forecast employee absenteeism using everything from prior medical history to wastewater data, so that nurse and construction worker unions can staff appropriately?
At Microsoft Azure Core, I work on AI systems for CPU performance intelligence, including large-scale data pipelines, retrieval, semantic search, and evaluation.
Outside of work, I build projects across backend systems, AI infrastructure, and applied machine learning.
| Organization | Role | Focus |
|---|---|---|
| Microsoft Azure Core | Software Engineering Intern | AI incident intelligence, hybrid retrieval, ranking, and evaluation |
| Apple Pay | Software Engineering Intern | Reliable distributed processing, concurrency controls, and payment analytics infrastructure |
| Capital One | Software Engineering Intern | Serverless data pipelines and an ML-powered mentor-matching application |
| HealthTrends.AI | Founding ML Engineer | Time-series forecasting and applied ML product development |
Winner, Google DeepMind × LangChain Multimodal Hackathon
A multi-agent platform for optimizing ad creative. Pixel combines visual-attention modeling, competitor retrieval through a Pinecone RAG index, and Gemini-generated variants scored by predicted attention lift.
A hybrid music recommendation engine that routes natural-language queries between ChromaDB semantic search and TF-IDF plus popularity-weighted ranking over live Spotify data.
A custom operating system built in C, exploring low-level systems programming, memory management, and operating-system design.
Languages: Python, Java, Kotlin, TypeScript, SQL, C
Backend and distributed systems: Spring Boot, FastAPI, PostgreSQL, DynamoDB, Kafka, gRPC, Kubernetes, Docker, AWS, Azure, Datadog
AI/ML systems: LLM agents, RAG, hybrid retrieval, semantic search, vector databases, and evaluation pipelines (LangChain, Gemini, Pinecone, ChromaDB)
I'm always interested in conversations about backend systems, infrastructure, and ambitious technical projects.