Principal Research Engineer, Model Training & Post-Training

📋 Description Own the model-improvement roadmap across capability, reliability, EI, cost. Lead training and post-training strategy (SFT, RLHF, RLAIF, reward modeling). Drive architecture/optimization for transformer-based and hybrid models. Lead large-scale training on distributed GPU clusters (1000+ GPUs). Define data strategy: curation, deduplication, decontamination, human-in-the-loop. Build evals and release-quality systems (evals, gates, readiness). 🎯 Requirements Experience leading large-scale LLM or foundation-model training/post-training. Deep experience with transformer models, hybrid architectures, distributed training. Post-training methods: SFT, RLHF, DPO, GRPO, RLAIF, reward modeling. Operate large GPU clusters (1000+ GPUs) and distributed training infra. Strong systems instincts around throughput, cost, reliability, observability. Judgment on data quality, evaluation design, release readiness, and model behavior. PhD in CS/ML/AI or equivalent practical experience. 🎁 Benefits Diverse medical, dental and vision options 401k matching Unlimited paid time off Parental leave and caregiver flexibility Visa support for international Bay Area employees