Member of Technical Staff (Post-Training)

Join our team as a Member of Technical Staff, Post-Training. In this high-ownership role, you will define and build a new organization focused on model improvement and reliable systems. You will partner with researchers, domain experts, and customers to turn ambiguous post-training questions into experiments, evaluation frameworks, data pipelines, and products. Your work will have a significant impact on our technical direction, operating culture, and the reusable systems we build.

• Definir y construir una nueva organización enfocada en la mejora de modelos de inteligencia artificial, trabajando en estrecha colaboración con investigadores y expertos en el dominio.
• Diseñar sistemas y metodologías de post-entrenamiento para modelos de frontera, incluyendo el ajuste fino supervisado, el aprendizaje por refuerzo y la optimización de preferencias.
• Identificar patrones repetibles en los compromisos y convertirlos en software y plataformas reutilizables, contribuyendo a la mejora continua de los sistemas.

• We care more about demonstrated research capability, technical judgment, and a builder’s mindset than a specific title, degree, or career path
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership
• Strong Python skills and the ability to write clean, efficient, scalable software
• Experience designing systems—not only implementing specifications—including the ability to make tradeoffs around quality, scale, reliability, and reuse
• 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work. Relevant experience may include RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments
• Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows
• Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers
• Sound experimental judgment: you can form hypotheses, choose meaningful metrics, diagnose failures, and distinguish signal from noise
• Building or operating large-scale ML training, inference, data, or evaluation systems
• Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks
• Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems
• Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems or AI research
• Experience productizing research or repeated customer work into robust, reusable platforms