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 such as 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 - San Francisco, CA; New York City, NY; Seattle, WA
Más vacantes en AnthropicSalary
USD 350,000 - 500,000
Requirements
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
- Ability to balance research exploration with engineering rigor and operational reliability
- Proficiency in Python, deep learning frameworks, and distributed computing
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Field relevant to the role as demonstrated through coursework, training, or professional experience
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 computingLarge Language Models (LLMs)Constitutional AIRLHFalignment methodologies
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