Research Scientist / Research Engineer, Pre-training

Anthropic Anthropic · AI Frontier · AI Research & Engineering

Research Engineer role focused on the pre-training of large language models, involving research into model architecture, algorithms, data processing, and optimizer development, as well as scaling training infrastructure and developing dev tooling. Requires advanced degree, strong software engineering skills, and familiarity with large-scale ML and deep learning frameworks.

What you'd actually do

  1. Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
  2. Independently lead small research projects while collaborating with team members on larger initiatives
  3. Design, run, and analyze scientific experiments to advance our understanding of large language models
  4. Optimize and scale our training infrastructure to improve efficiency and reliability
  5. Develop and improve dev tooling to enhance team productivity

Skills

Required

  • Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
  • Strong software engineering skills
  • Expertise in Python
  • experience with deep learning frameworks (PyTorch preferred)
  • Familiarity with large-scale machine learning, particularly in the context of language models
  • Ability to balance research goals with practical engineering constraints
  • Strong problem-solving skills
  • results-oriented mindset
  • Excellent communication skills
  • ability to work in a collaborative environment

Nice to have

  • Work on high-performance, large-scale ML systems
  • Familiarity with GPUs, Kubernetes, and OS internals
  • Experience with language modeling using transformer architectures
  • Knowledge of reinforcement learning techniques
  • Background in large-scale ETL processes
  • pair programming
  • collaborative work
  • learn more about machine learning research
  • align state of the art models with human values and preferences
  • understand and interpret deep neural networks
  • develop new models to support these areas of research
  • AI safety
  • general progress in AI

What the JD emphasized

  • proven track record of building complex systems
  • Familiarity with large-scale machine learning
  • Ability to balance research goals with practical engineering constraints
  • Care about the societal impacts of your work
  • Work on high-performance, large-scale ML systems
  • Experience with language modeling using transformer architectures
  • Knowledge of reinforcement learning techniques
  • Background in large-scale ETL processes
  • significant software engineering experience
  • Are results-oriented with a bias towards flexibility and impact
  • Willingly take on tasks outside your job description to support the team
  • Are eager to learn more about machine learning research
  • Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
  • View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
  • Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Other signals

  • developing the next generation of large language models
  • Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
  • Design, run, and analyze scientific experiments to advance our understanding of large language models
  • Optimize and scale our training infrastructure to improve efficiency and reliability