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Vamsi Krishna.I keep models useful after they ship.

I'm a master's student at Penn who likes the unglamorous half of ML: what happens to a model after launch. Last summer at Google I worked on keeping YouTube's safety classifiers current as content drifts, using weight merging instead of replay buffers. This fall I'm at Amazon AGI, on the platform people use to customize models. I grew up in Chennai around ship brokers, which is why my side projects keep drifting back to the sea.

Building
An agent that lives in a ship broker's inbox and works the deals. A real broking desk is the first user.
Believe
The interesting problems start after the model ships. Drift, forgetting, serving cost.
Learned most from
A startup that didn't find product-market fit.
Reading
Philosophy, ancient Indian history, and a lot of science fiction. Best thing I've read on the sea: The Ocean of Churn by Sanjeev Sanyal.
Long game
Build something of our own. Big tech is where I'm learning to do it properly.
Ask me about
Model merging, continual learning, AI alignment, or recursive self-improvement.

Now

  1. Google

    YouTube Trust & Safety, GenAI Guardrails

    May 2026 – Aug 2026 · Mountain View, CA

    Software Engineering Intern

    Benchmarked 9 architectures and 8 weight-merging methods (SLERP, LERP, TIES, MagMax) in JAX on Gemma 3. Finding: consolidate in data space for encoders and weight space for decoders.

    Handed off a joint multi-task encoder at 98.33% macro ROC-AUC: +8.5 pp recall over per-policy experts at 95% precision, replacing a 4x ensemble at 1x cost and lifting worst-policy recall from 6.49% to 62.86%.

    Designed the OOD adaptation playbook (sequential zero-replay fine-tuning, then SLERP merge), cutting adaptation compute over 60% with no legacy-policy regression, and an agent that runs it end to end. First-author paper under review, NeurIPS 2026 workshop.

  2. Amazon AGINow

    AWS SageMaker

    Sep 2026 – Dec 2026 · Sunnyvale, CA

    Software Development Engineer Intern, ML

    Model-customization platform: production workflows for fine-tuning, preference optimization, prompt engineering, and custom evals, agnostic to model, inference stack, and compute.

  3. S2T AI

    AI-powered investigations

    Dec 2024 – Aug 2025 · Singapore

    Software Engineer

    Owned the company's agentic AI from prototype to customers: knowledge-graph RAG over documents and databases, a multi-agent assistant with tool calling and generative UI, an AI ETL service, and 8 Kubernetes microservices via Azure DevOps.

  4. Vessel Match

    Incubated at Nirmaan, IIT Madras

    May 2024 – Jun 2025 · Chennai, India

    Founder & CEO

    Maritime matching startup incubated at Nirmaan, IIT Madras. Event-driven services on AWS, FastAPI, Next.js. 200+ vessels and 150+ cargo listings a day in trials, 5 industry partnerships, INR 7L seed. No product-market fit; the most useful year I've had.

    Product demo ↗

Projects

Hackathons, coursework, weekends

Papers

Adapt, Merge, Validate: Zero-Replay Continual Learning for Multi-Policy Safety Classifiers
CL4FMAgents Workshop, NeurIPS 2026 · under review

Education

University of Pennsylvania
MSE Data Science · 2025 – 27 · GPA 3.91
IIT Madras
BS Data Science & Applications · 2021 – 25 · GPA 3.91
SSN College of Engineering
B.E. Mechanical Engineering · 2020 – 24 · Department topper, CGPA 9.21
NUS School of Computing
Deep learning program · 2023 · Best performer

Contact

Say hello.

Email is best. I reply to anything with a real question in it.

nkvk@engineering.upenn.edu

Open to 2027 new grad roles in ML systems, AI infrastructure, and agents.