Find the technical expert who built it.

Vetted technical experts, precisely matched: the engineers, researchers and maintainers behind the technology your client is asking about, with their work as proof.

Experts on LLM inference serving

  • Zihao Ye
    @yzh119
    May
    Jun
    Jul
    Aug
    Sep
    Oct
    Nov

    CUDA engineer shipping kernels for graph ML and LLM serving

    Works on GPU kernels and graph deep learning infrastructure. Commits to dmlc/dgl, a package for deep learning on graphs, and contributes to flashinfer-ai/flashinfer, a kernel library for serving large language models.

  • Lequn Chen
    @abcdabcd987
    May
    Jun
    Jul
    Aug
    Sep
    Oct
    Nov

    ML systems engineer on Perplexity's LLM serving stack

    Delivers distributed ML systems infrastructure at Perplexity. Lands changes in flashinfer-ai/flashinfer, a kernel library for serving large language models, and pushes to punica-ai/punica, a server for multiple LoRA finetuned language models.

  • Yineng Zhang
    @zhyncs
    May
    Jun
    Jul
    Aug
    Sep
    Oct
    Nov

    Sr. Director building LLM serving systems at Together AI

    Drives LLM serving infrastructure at Together AI. Pushes to sgl-project/sglang, a serving framework for large language models, and works on kvcache-ai/Mooncake, a serving platform for large language models.

Use cases

Custom recruiting

  • Builders of AI content licensing and crawler controls
  • Engineers who scaled GPU inference on vLLM
  • Early contributors to the Model Context Protocol
  • CUDA kernel experts for LLM training at scale
  • Who built Apache Kafka tiered storage?

Vetting and compliance

  • Is this expert’s claimed experience backed by their work?
  • When did this expert leave the company we are diligencing?
  • Who worked closely with this expert for a second call?
  • Brief me on this expert’s work before the screening call

For research, recruiting and expert sourcing teams.

The right technical expert for every project, within hours.

Today
Former executives and users, custom recruits and profiles that are years out of date.
With Vamo
The people who built the technology, vetted by their work before the screening call.

How it works

  1. Step 1

    Ask in plain words

    Describe who you want in Claude, or send it to the API: a place, a company, a repo or the kind of work.

  2. Step 2

    Get builders ranked by their work

    Each person comes back with the repos they own and contribute to, their contribution garden, a cracked score and a one-line why.

  3. Step 3

    Use it where you work

    Keep a shortlist, write the fields to your CRM, or build them into your product over the API.

Run it in Claude, or build on the API. See how Vamo compares with Exa.

Find the right expert for your hardest project.