Member of Technical Staff - Post Training

Required Qualifications

• Reinforcement Learning in Practice You have actually run RL on language models. You have implemented reward models, dealt with reward hacking, tuned KL penalties, and shipped models that are meaningfully better as a result. You understand the theory and you have applied it.
• Post-Training Engineering Depth Hands-on experience with data generation and evaluation for LLM post-training. You have run SFT, preference alignment, and RL workflows on real models and you know where these pipelines break.
• Framework Proficiency Strong Python and PyTorch proficiency with hands-on experience optimizing training pipelines. Experience with DeepSpeed, FSDP, vLLM, or similar frameworks for efficient model training and inference.
• Data Quality Instinct Strong intuition for what good training data looks like. Experience designing and executing data generation, filtering, curation, and quality assessment processes at scale.
• End-to-End Thinking You reason across data generation, training, alignment, and evaluation as a single system. You do not optimize one stage in isolation from the others.
• Customer and Communication Fluency Comfortable working directly with enterprise customers. You can translate between customer needs and internal technical teams, push back when needed, and be trusted as the technical owner of a delivery.
• Continual Learning Familiarity Familiar with the challenges of continual and lifelong learning in neural networks. You have thought seriously about catastrophic forgetting and how to build models that stay current without degrading.
• Great Team Spirit A mission-driven approach to engineering, valuing clear communication, hands-on execution, and collective success over individual silos.