Brisbane, Australia · remote-friendly · Open to roles & contracts
Lee Goymer
Systems generalist · Applied-AI engineer
I design, build, and operate AI systems end-to-end — evaluation labs, GPU schedulers, knowledge archives, voice assistants — in production, on hardware I administer myself.
The system
Everything below runs on one Ubuntu server with an RTX 3090, plus a ROCK 5B+ at the edge — built and operated solo over a twelve-month build year. Monitored, scheduled, backed up off-site, and watched by a deadman switch.
Case studies
One box, twenty services Operating daily
A personal AI platform on a single RTX 3090 server — built, instrumented, and operated solo with the discipline of a small infra team.
Open-model evaluation lab Daily pipeline
A self-hosted answer to "what's the best open model for X?" — 8,351 models tracked, benchmarked across modalities, with psychometric test design and an LLM judge I actually calibrated.
ARK — verified knowledge compression Verified archive
An offline, provenance-rich archive of open human knowledge — compressed, integrity-verified end-to-end, and queryable without the internet.
CTA3 — an LLM solver for community riddles Research engine
A full-stack research database and agentic solver for osu!'s hardest community puzzle tournament — 1.45 million scores indexed, every miss forensically analyzed.
GPU timeshare — multi-tenant scheduling for one RTX 3090 In production
Eight GPU-hungry projects, 24 GB of VRAM, zero OOM wars — a lease API with priority aging, reversible preemption, and an enforcer that audits every process it kills.
More from the lab
SAM Foreground API
Stable service
Production image-segmentation service — SAM 2.1 + depth-aware candidate ranking, benchmarked to 0.995 mIoU on a human-evaluated suite.
Life Coach — privacy-first personal AI
Running daily
A coaching agent with months-long memory — temporal knowledge graph, contradiction resolution, and a three-lane privacy architecture that keeps the private parts local.
Easy Transcription
Stable service
Self-hosted speech-to-structured-notes — browser recording, live WebSocket ASR, and LLM post-structuring into summaries and action items.
Edge multimodal node — ROCK 5B+
13 services live
A complete multimodal AI stack on a 6-TOPS ARM board — ASR, TTS, VLM, and diversity-sampled capture, with models quantized and converted for the NPU by hand.
osu!catch Completion Tracker
Live on the internet
A public web app tracking ranked-beatmap completion for osu!catch players — OAuth login, 800+ requests/minute ingestion, live WebSocket progress.
The idea refinery
Research pipeline
A 12-stage adversarial pipeline that generates ideas, tries to kill them, rescues the salvageable, and scores survivors with deterministically-capped LLM judges.
Speech-enhancement benchmark harness
Methodology piece
How do you benchmark denoising when no clean reference exists? A fair-comparison harness for dashcam audio with ASR-hallucination detection as the quality signal.
Tab Veto'er
Working MVP
2,704 hoarded browser tabs triaged by an LLM that checks what I already own, already know, and actually need before it lets anything survive.
Also in the lab: Smart Home (Home Assistant ↔ RabbitMQ event bridge, Zigbee mesh, local voice — hardware phase in progress) · Vehicular Assistant (streaming ASR→LLM→TTS voice loop for the car, pluggable stage protocols with per-stage latency instrumentation) · Universal Critic (three-pass dialectic critique engine: critique → steelman → adjudicate) · Researcher (search-synthesis service with enforced source attribution) · Solar sufficiency modeling, dashcam processing, video compression pipelines, and a long archive of community infrastructure — including Minecraft community services (modpack distribution, server lists, vote bots) run for real users for years.
About
I'm a Computer Science graduate (Griffith University) and former Amazon SDE (Amazon Fresh — proactive order-issue remediation, event pipelines at US-grocery scale) who took a deliberate build year: a low-stakes day job to pay the bills, and every other waking hour turning a single RTX 3090 server into the twenty-service AI platform on this page — an evaluation lab tracking 8,000+ open models, a GPU scheduler with reversible preemption, a verified knowledge archive, voice assistants on edge silicon — all running 24/7 under real operational discipline: monitored, scheduled, backed up off-site, watched by a deadman switch.
Before that: software development and cyber-security work — two paid security research internships (formal verification of Solidity smart contracts at Griffith's Institute for Integrated and Intelligent Systems), a fintech penetration-testing engagement in Hong Kong, and a top-ranked first-year team finish in the Australian Cyber Security Challenge.
What I'm best at is the full loop: pick a hard, fuzzy problem; design the system; build it fast; measure it honestly; run it in production; then write down what's still broken. The case studies above each end with a "status & limits" note for exactly that reason — I'd rather you trust the numbers than admire the adjectives.
What I'm looking for: applied-AI engineering, platform work, forward-deployed/ solutions roles, founding-engineer seats, or contract prototyping — anywhere the job is "figure it out end-to-end and ship it."
Contact
Open to roles and contract engagements — applied AI, platform engineering, rapid prototyping, evaluation work.