Jordan Madison
Research Engineer building post-training, evaluation, and infrastructure for open-weight language models.
Interested in research engineering, ML engineering, and MTS roles.
I'm a Georgia Tech computer science graduate working at the intersection of machine learning, infrastructure, and security. I still play Rocket League and spend more time than I should wondering how many bits it takes to reach the optimum of a loss curve.
At Georgia Tech's Reasoning and Learning Group, I build systems that train language models to propose and evaluate changes to crystal structures. My work on CreSTAL connects materials-science tools with supervised fine-tuning, reinforcement learning, and evaluation. We study whether learning from individual edits helps models reach a target material property over successive attempts.
My work spans executable RL environments, post-training pipelines, HPC orchestration, and the debugging required to make these systems reliable. I also have a broader background in applied machine learning, cybersecurity, privacy, and malware reverse engineering.
Selected work
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CreSTAL · Post-training research
Built CreSTAL, extending LLMatDesign's crystal-editing workflow with supervised fine-tuning and reinforcement learning from executable feedback. Across four models evaluated on 1,000 held-out materials, RL produced mixed target-hit gains.
Read the case study → -
Atlas · Research infrastructure
A job orchestration layer for post-training research that turns reusable templates into repeatable SLURM training, evaluation, and data-generation workflows.
View the project → -
Cloudflare Radar · 0-to-1 product
Led 0-to-1 product work that turned global network telemetry into a public internet intelligence platform shipped in 12 weeks.
View experience →
Recent writing
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An application of self-improving harnesses to limit order placement.
#design , #finance , #investment , #agent , #meta
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A breakdown of an RL environment I designed after misreading a take-home assignment. The design is built around my reverse-engineering background and I liked it enough to write it up here.
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A LoRA debugging story about four embeddings that never got to learn anything. If you add tokens and freeze the base model, their embedding and LM-head rows still need to train.
#post-training , #sft , #lora , #tokenizers , #ai-assisted
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Reading GPU telemetry on a colocated GRPO run: periodic utilization dips, memory-access spikes, and a bottleneck story of mine that didn't survive contact with the code.
#rl , #post-training , #grpo , #vllm , #ai-assisted
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A debugging story about special tokens, GRPO, and the skip_special_tokens flags scattered across your RL stack. When your SFT'd model nails the format in eval, then falls apart the moment it enters the GRPO rollout.
#rl , #post-training , #grpo , #tokenizers , #ai-assisted