Location: Orange County, CA (Pacific Time)
Remote: Yes (preferred); hybrid Orange County also fine
Willing to relocate: No
Technologies: Python, Go, SQL | FastAPI, Django, Vue, HTMX | Postgres, TimescaleDB, DuckDB, Redis | Temporal, Airflow, NATS | AWS (S3/RDS/EC2/Redshift), Docker, Kubernetes | Prometheus/Grafana | LLMs in production (RAG, eval, orchestration, structured extraction)
Resume/CV: https://brojonat.com/Jon_Brown_Resume.pdf
Website / writing: https://brojonat.com
GitHub: https://github.com/brojonat
Email: [email protected]
PhD-trained engineer/manager (astronomy by training -- data is data). Currently Senior Data Science Manager at Hyundai Motor America's North American Safety Office, where I build large-scale vehicle telemetry pipelines, LLM-driven classification and RAG over enterprise data, and led department-wide LLM/AI adoption from technical workshops to production SDLC.
Previously managed a data science team at HeadSpin: built data science pipelines, killed error-prone spreadsheet workflows with proper APIs and dashboards, reimplemented the flagship data product to reduce costs, latency, and churn.
Looking for senior data / platform sorts of roles. Equally happy IC or manager -- I just want to ship. Open to full-time or fractional/contract; happy to even work on small projects you want to send my way to help get to know each other.
I build a lot on my own time, and the side projects are probably the best read on how I work:
- forohtoo -- Go service for awaiting Solana payments via SSE/NATS, with a clean `Await(memo, amount)` SDK. Writeup: https://brojonat.com/posts/forohtoo/
- IncentivizeThis (https://incentivizethis.com) -- bounty-based authentic ad platform using LLM-as-judge across Reddit, YouTube, Twitch, Instagram, HN, TripAdvisor. Writeup: https://brojonat.com/posts/incentivizethis/
- If I Go Missing (https://if-i-go-missing.com) -- dead-man's-switch service (Go + Temporal + Postgres + Twilio + Stripe).
- IYBI ("If You Build It [They Will Come]", https://iybi-twc.com) -- websites for independent workers, managed entirely by email. Customer pays via Stripe, emails an AI agent (Claude + Cloudflare Email Worker + R2 + K8s CronJob), the agent researches them, builds the site, and handles ongoing updates. My own consulting site runs on it.
Mostly Go or Python services running on my personal k8s cluster. Happy to talk shop or small projects we could work on together to assess fit.
For my demos I've been running 3 minute sessions but I'm planning to run 30-60 minute sessions depending on the scenario. I want to see people push the boundary of what can be done in an hour (with an agent or without, it's up to the contestant!) and ultimately have the match VODs serve as entertainment but also reference examples for "good" development workflows.
I'm working on a competitive coding gameshow. I'm imagining a combination of great british bakeoff, battle bots, and dota. Basically contestants get dropped into a fully equipped dev machine (all the bells and whistles one could want/expect including neovim, agent harnesses, cool styling, etc and if you want you can always clone your dotfiles and stow them!). I've gotten a decent prototype that live streams from Fly.io sprites to twitch, and I'm able to voice over or have OpenAI do commentary on the match. I've got a demo here: https://www.twitch.tv/videos/2792893261. Still a ways to go, but it seemed like a fun way to tinker with Sprites.
It is already very plausible (and has been since the 1950s) without the advent of LLMs. This is just another layer on top of the preexisting and very plausible existential threats we already face.
Location: Orange County, CA (Pacific Time)
Remote: Yes (preferred); hybrid Orange County also fine
Willing to relocate: No
Technologies: Python, Go, SQL | FastAPI, Django, Vue, HTMX | Postgres, TimescaleDB, DuckDB, Redis | Temporal, Airflow, NATS | AWS (S3/RDS/EC2/Redshift), Docker, Kubernetes | Prometheus/Grafana | LLMs in production (RAG, eval, orchestration, structured extraction)
Resume/CV: https://brojonat.com/Jon_Brown_Resume.pdf
Website / writing: https://brojonat.com
GitHub: https://github.com/brojonat
Email: [email protected]
PhD-trained engineer/manager (astronomy by training -- data is data). Currently Senior Data Science Manager at Hyundai Motor America's North American Safety Office, where I build large-scale vehicle telemetry pipelines, LLM-driven classification and RAG over enterprise data, and led department-wide LLM/AI adoption from technical workshops to production SDLC. Previously managed a data science team at HeadSpin: built data science pipelines, killed error-prone spreadsheet workflows with proper APIs and dashboards, reimplemented the flagship data product to reduce costs, latency, and churn.
Looking for senior data / platform sorts of roles. Equally happy IC or manager -- I just want to ship. Open to full-time or fractional/contract; happy to even work on small projects you want to send my way to help get to know each other.
I build a lot on my own time, and the side projects are probably the best read on how I work:
- forohtoo -- Go service for awaiting Solana payments via SSE/NATS, with a clean `Await(memo, amount)` SDK. Writeup: https://brojonat.com/posts/forohtoo/
- IncentivizeThis (https://incentivizethis.com) -- bounty-based authentic ad platform using LLM-as-judge across Reddit, YouTube, Twitch, Instagram, HN, TripAdvisor. Writeup: https://brojonat.com/posts/incentivizethis/
- If I Go Missing (https://if-i-go-missing.com) -- dead-man's-switch service (Go + Temporal + Postgres + Twilio + Stripe).
- IYBI ("If You Build It [They Will Come]", https://iybi-twc.com) -- websites for independent workers, managed entirely by email. Customer pays via Stripe, emails an AI agent (Claude + Cloudflare Email Worker + R2 + K8s CronJob), the agent researches them, builds the site, and handles ongoing updates. My own consulting site runs on it.
Mostly Go or Python services running on my personal k8s cluster. Happy to talk shop or small projects we could work on together to assess fit.
I run my own temporal service in my k8s cluster; this setup is the backbone for almost all my applications. For simplicity, I opted for the postgres backend. You still need to run the 4 (?) other service (history, matching, frontend, ui, maybe others, definitely others if you want observability with prometheus/grafana, and tad bit more complexity if you want tailscale to get in there and poke around).
They ship Helm charts so reality is somewhere between "helm deploy" and "substantial ops burden". I don't have to touch it very frequently, but that is not to say I don't have to touch it. There's occasional releases and there have been times where (probably due to my inexperience with helm) I botched an upgrade and lost some data. And I've been on this journey for years; when I first started, they didn't have a Python SDK and it was one of my (many) excuses to learn Go. But anyway to your point, yes, if you're comfortable with k8s and Helm then you shouldn't have much of a problem running hundreds of thousands of workflows; if you want to really push the throughput and optimize cost you probably need to get creative the individual services and look into cassandra (maybe? idk).
They made a big point of explicitly advertising this as a feature with the GPT-5 rollout, no? Routing to cheaper models/less reasoning depending on the input prompt.
Exactly! It must be exhausting to have this huge preoccupation with determining if something has come from an LLM or not. Just judge the content on it's own merits! Just because an LLM was involved doesn't mean the underlying ideas are devoid of value. Conversely, the fact that an LLM wasn't involved doesn't mean the content is worth your time of day. It's annoying to read AI slop, but if you're spending more effort suspiciously squinting at it for LLM sign versus assessing the content itself, then you're doing yourself a disservice IMO.
You could make the case uv falls in this category (I just prefix all my pip commands with uv) though we have yet to see if astral will become a "successful business"; I'm hoping they pull it off.