7 hours ago
Responsibilities
- Set the company’s technical and research strategy for a proprietary AI training and evaluation platform.
- Build data infrastructure connecting briefs, submissions, generation traces, human decisions, critiques, and campaign outcomes.
- Develop systems to evaluate, rank, critique, generate, and improve creative work.
- Design campaign-budget allocation policies that support useful data collection.
- Lead decisions involving frontier models, fine-tuning, post-training, reinforcement learning, and original research.
- Build reliable infrastructure for creator workflows, campaigns, experimentation, model training, and inference.
- Set standards for data quality, consent, privacy, measurement, and model evaluation.
- Recruit and lead the initial engineering and research team while working with creators and brands.
Requirements
- At least 3 years of relevant experience.
- Hands-on experience building preference or reward models, including RLHF, ranking subjective outputs, or evaluation systems.
- Experience shipping production products, building production ML infrastructure, and owning a model, evaluation, or infrastructure program end to end.
- Background in causal inference on observational data, including selection bias, off-policy or counterfactual evaluation, and adaptive experimentation.
- Experience with advertising, marketplaces, recommenders, or ranking systems tied to business outcomes and noisy or delayed feedback.
- Strong Python and production machine learning skills.
- Experience with bandits, sequential allocation, online experimentation, multimodal systems, or agents is valuable.
- Ability to recruit and lead technical talent and commit full time to an early-stage co-founder role.
Benefits
- Approximately 28% equity as an equal co-founder.
- Cash compensation discussed based on circumstances and fit.
- On-site work in San Francisco, California.
- Full-time founding role with an expected six- to seven-day workweek.
Tech Stack
Categories
AI ResearchML Engineering