Research Engineer, Science of Scaling
London, UKFull-TimeMid-levelSoftware Engineering
About Anthropic
- Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
- Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You'll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.
Responsibilities
- Conduct research intro the science of converting compute into intelligence
- Independently lead small research projects while collaborating with team members on larger initiatives
- Design, run, and analyze scientific experiments to advance our understanding of large language models
- Optimize training infrastructure to improve efficiency and reliability
- Develop dev tooling to enhance team productivity
You may be a good fit if you
- Have significant software engineering experience and a proven track record of building complex systems
- Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
- Are proficient in Python and experienced with deep learning frameworks
- Are results-oriented with a bias towards flexibility and impact
- Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team
- View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximize impact
- Care about the societal impacts of your work and have ambitious goals for AI safety and general progress
Strong candidates may have
- Experience with JAX
- Experience with reinforcement learning
- Experience working on high-performance, large-scale ML systems
- Familiarity with accelerators, Kubernetes, and OS internals
- Experience with language modeling using transformer architectures
- Background in large-scale ETL processes
- Experience with distributed training at scale (thousands of accelerators)
Strong candidates need not have
- Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box
- Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well
- An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply
- The annual compensation range for this role is listed below.
- For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
