Requirements

• Two or more years of hands-on experience training, post-training, or evaluating LLMs at scale
• Direct experience with at least one of: large-scale pretraining, post-training (SFT, RL), LLM evaluation design and statistical analysis, or distributed training/inference systems
• Research taste: ability to identify what's worth trying next, design ablations that change decisions, and update beliefs from evidence
• Strong engineering skills valued: GPU kernel development, lower-level PyTorch internals, or published work on a frontier-style LLM result

What You'll Be Doing

• Work on every stage of the model development loop
• Tasks include tokenizer training, dataset curation and mixing, pretraining, post-training, evaluation design, inference, and live trading
• Pursue impactful directions as part of a small, focused team with minimal bureaucracy

Perks and Benefits

• Base salary range: 200,000 to 300,000 USD per year
• Eligible for discretionary performance-based bonuses
• Competitive benefits package