Naz V.'s resume

summary

self-taught in software development and mathematics
and built a number of projects and companies in the crypto space

experience

renft labs (aka zero to one) co-founder and CTO

oct. 2020 – jan. 2025

nft rentals platform

tested the viability of nft rentals idea by participating in 5+ hackathons
took prize money in all of them
with hackathon prize money, we hired our first employees
built an EVM collateralized NFT rental protocol, code here
we raised $6.5m in pre-seed and seed funding from crypto VCs
led a team of 5 developers at peak
built a number of quick experiments around NFT tech. some can be found here
one experiment was around enabling tenant rights for crypto punks. code here. which received media coverage
built a new collateral-free rental protocol
we partnered with a leading Brazilian mobile game studio behind Castle Crush game (50+ million downloads)
this was a tremendous success and led to over 500k onchain rentals
we iterated protocol yet again, this time it was based on smart wallet functionality. renters would directly receive NFT into their wallet but we access controlled what they were allowed to do
when it became clear nft rentals won't find a pmf in the near term, we started pivoting
built a first competitor to pump.fun, with a team of 2 engineers and me, in 4 weeks here

media coverage

rkl co-founder and CTO

aug. 2021 – may 2023

mobile game powered by nfts

we were approached by Sandbox CMO to help him deliver an nft collection powered mobile game
there were a lot of synergies with renft, we envisioned rental integration just like with Castle Crush
the mint went very smoothly and a lot of high profile celebrities and NBA players started interacting with our community
this uptick in interest led to attention from prominent investors that persuaded us to raise funding
we raised over $6.5m in funding from investors and NBA players (Stephen Curry and Paul George among others)
our collection generated over $70m (20k+ ETH) in traded onchain volume
generated revenue of over $5m in contracts deployed and royalties collected
during my cto tenure, we were constantly praised for the smoothness and reliability of our services. we never had any smart contract bugs. this was during the times where some teams would ship buggy smart contracts irrevocably locking $30m+ of users' funds
we ran a number of tech experiments. we were the first collection to offer onchain nft renaming service. another, was building an onchain marketplace, code here
led a team of 5 developers at peak
rumble kong league game can be found on ios and google play store

media coverage

dolfin financial quantitative developer

jan. 2018 – sep. 2019

wealth management company

built internal quant tools in Python, see here
best piece of work I have done here was a multi-asset portfolio's risk factor decomposition model, see here. it measured portfolio's sensitivity to different risk factors and could simulate historical black swan events based on your portfolio's exposure to those risk factors
got promoted after a year at the job

omni partners / bsl quantitative developer

june 2016 – nov. 2017

systematic investment fund out of ucl

this role needs some background context
just after completing my first MSc I had to work for an insurance company for a year, i realised quickly i have made some bad decisions when it comes to my degree choices
at this stage i had no computer science nor mathematics background
so i started learning mathematics in the evenings after work
applied and got accepted to UCL to study some advanced math
with no formal math education jumping into things like black-scholes has been very challenging, to say the least. but this was also the most rewarding year of my life
we had a summer internship as part of the course, I was assigned a company I
was not happy about. negotiated a switch and interviewed with banking science ltd (bsl)
after completing the internship, I was offered a full-time role
I was the first graduate to join the bsl team to help them build their strategy. when i joined, the team was comprised of a cto, a head quant and me
this job marked the beginning of my software / quant career
was responsible for the pre-screening stock selection stage and data handling
built all the data processing pipelines
built a Python portfolio analysis tool that generated a pdf report analysing returns, portfolio composition and so on
oversaw a machine learning intern
helped interview new hires
got promoted after 6 months of work

projects

solana high frequency trading engine

oct. 2024 – present

built and ran a self-funded Solana HFT Rust engine
achieved ~50 µs tick-to-trade (market-data → order-transmit)
implemented a durable nonce multi-path routing through independent providers and direct to leader submission
CPU pinning for critical threads
state reconciliation: periodic RPC/account diffs checks to detect and fix desync
asynchronous, non-blocking logging
real-time sidecar indexer for custom risk metrics and feature calculations
open-position management driven by a hazard statistical model for exit decisions
low-latency shred ingestion and reconstruction (deshredding) with multiplexed receivers to minimize data-receive latency
Solana validator majority stake co-location to reduce propagation and leader-path latency
consistently landed transactions ahead of 99% of other snipers

metrics: p50/p99 tick-to-trade 50 µs / 150 µs · leader inclusion 70% · zero-slot misses 10% · $500k volume traded

coinbase pro market-making engine

oct. 2019 – jan. 2021

built and ran a self-funded python market-making bot on Coinbase
wrote a concurrent (multi-threaded) engine: websocket/REST I/O, quoting, execution, monitoring, and logging, with clean, fault-tolerant shutdowns
maintained a local L2 order book by applying snapshots and incremental updates; used it to compute depth/spreads and mark-out
implemented an adaptive quoter that prices at meaningful depth; slows/pauses quoting during reconciliation or heavy execution to avoid desync
built a robust execution layer with websocket→REST fallbacks, retries with randomized backoff, dynamic sizing based on capital
added a state manager that mirrors balances in real time, reconciles dropped trade messages via REST polling, persists net-fills and PnL (DynamoDB)
embedded risk controls (simple go/no-go checks and tripwires): spread/vol/volume/drawdown thresholds that switch the engine between TRADE / WAIT / STOP and auto-flush positions when limits are breached
productionized with CI/CD (CircleCI), Docker, AWS ECR/ECS, runtime secrets via SSM, CloudWatch logs, and DynamoDB-backed configs/backups with point-in-time recovery

metrics: ~$1k capital → ~$500k monthly trading volume · realized after fees 10 bps · 30s mark-out +2 bps (median) · ~17× daily turnover · quoted deep in the book · inventory bounds ±20%

extra

worked with ucl researchers on the first drafts of scientific work that explored amm based dexes. first draft here; i contributed all the code, as well as, understanding of how automated market makers (AMMs) work
i occasionally blog here: blog.naz.ooo