Used Tools & Technologies
Not specified
Required Skills & Competences ?
Python @ 6 Distributed Systems @ 4 Communication @ 4 Debugging @ 4Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The team consists of researchers, engineers, policy experts, and business leaders collaborating to build beneficial AI systems.
Responsibilities
- Implement and optimize post-training techniques at scale on frontier models
- 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
Requirements
- Strong software engineering skills with experience building complex ML systems
- Comfortable 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
- Adept at analyzing and debugging model training processes
- Collaboration skills across research and engineering disciplines
- Ability to navigate ambiguity and progress in fast-moving research environments
- Interest in AI safety and responsible deployment
- Experience with large language models (LLMs) is a significant plus
- Proficiency in Python, deep learning frameworks, and distributed computing
- Bachelor’s degree in related field or equivalent experience
Benefits
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours
- Work environment includes a lovely office space for collaboration
- Visa sponsorship available with immigration lawyer support
- Inclusive and diverse work culture promoting underrepresented groups
Additional Info
- Hybrid office policy: staff expected to be onsite at least 25% of the time
- Emphasis on collaboration, communication, and impactful AI research
- Focus on large-scale, empirical AI research with long-term goals