Week 2025-W10

Anthropic· 3 roles

• Machine Learning Systems Engineer, RL Engineering Post-train · Engineering 9
  This role focuses on building, maintaining, and improving the critical algorithms and infrastructure for training AI models, specifically using RLHF and other advanced techniques. The goal is to enhance the performance, robustness, speed, reliability, and usability of these training systems to enable breakthroughs in AI capabilities and safety.
• Machine Learning Systems Engineer, Encodings and Tokenization Data · Engineering 9
  Machine Learning Systems Engineer focused on developing and optimizing encodings and tokenization systems for Anthropic's Finetuning workflows, acting as a bridge between Pretraining and Finetuning teams. This role is crucial for improving model training efficiency and performance, enabling researchers to experiment with new tokenization methods, and ensuring the reliability and interpretability of AI systems.
• Machine Learning Systems Engineer, Model APIs Eval Gate · Engineering 8
  Machine Learning Systems Engineer focused on building and maintaining Model Evaluations infrastructure and Research Inference APIs/infrastructure to enable researchers to effectively evaluate models and conduct inference tasks, directly impacting AI advancement.