Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.
Research Engineer, Production Model Post-Training en Anthropic
Híbrido - Zürich, CH
Más vacantes en AnthropicRequirements
Skills
- Strong software engineering skills with experience building complex ML systems
- Comfortable working with large-scale distributed systems and high-performance computing
- Experience with training, fine-tuning, or evaluating large language models
- Proficiency in Python, deep learning frameworks, and distributed computing
- Experience with LLMs
- Keen interest in AI safety and responsible deployment
- Ability to balance research exploration with engineering rigor and operational reliability
- Adept at analyzing and debugging model training processes
- Excellent collaboration across research and engineering disciplines
- Adaptability to changing priorities and ambiguity
Responsibilities
- Implement and optimize post-training techniques at scale on frontier models
- Conduct research to develop and optimize post-training recipes that directly improve production model quality
- Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
- Develop tools to measure and improve model performance across various dimensions
- Collaborate with research teams to translate emerging techniques into production-ready implementations
- Debug complex issues in training pipelines and model behavior
- Help establish best practices for reliable, reproducible model post-training
Technologies
PythonDeep learning frameworksDistributed computingConstitutional AIRLHF
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