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.

You want to build large scale ML systems from the ground up. You care about making safe, steerable, trustworthy systems. As a Software Engineer, you'll touch all parts of our code and infrastructure, whether that's making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling. You're excited to write code when you understand the research context and more broadly why it's important.

Note: This is an "evergreen" role that we keep open on an ongoing basis. We receive many applications for this position, and you may not hear back from us directly if we do not currently have an open role on any of our teams that matches your skills and experience. We encourage you to apply despite this, as we are continually evaluating for top talent to join our team. You are also welcome to reapply as you gain more experience, but we suggest only reapplying once per year.

You may be a good fit if you:

Have significant software engineering experience
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair!)
Want to learn more about machine learning research
Care about the societal impacts of your work

Strong candidates may also have experience with:

High performance, large-scale ML systems
GPUs, Kubernetes, Pytorch, or OS internals
Language modeling with transformers
Reinforcement learning
Large-scale ETL
Strong candidates may also have experience with

Have security and privacy best practice expertise
Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
Low level systems experience, for example linux kernel tuning and eBPF
Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems

Representative projects:

Optimizing the throughput of a new attention mechanism
Comparing the compute efficiency of two Transformer variants
Making a Wikipedia dataset in a format models can easily consume
Scaling a distributed training job to thousands of GPUs
Writing a design doc for fault tolerance strategies
Creating an interactive visualization of attention between tokens in a language model

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

Annual Salary:

£225,000—£325,000 GBP

Logistics

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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While we encourage people to use AI systems during their role to help them work faster and more effectively, please do not use AI assistants during the application process. We want to understand your personal interest in Anthropic without mediation through an AI system, and we also want to evaluate your non-AI-assisted communication skills. Please indicate 'Yes' if you have read and agree.

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Which teams or projects are you most interested in? (Note: if none of the teams you select are hiring, we won't proceed with your application at this time, although we may reach out if those teams open roles in the future.)

  Pre-training — The Pre-training team trains large language models that are used by our product, alignment, and interpretability teams. Some projects include figuring out the optimal dataset, architecture, hyper-parameters, and scaling and managing large training runs on our cluster.
  AI Alignment Research — the Alignment team works to train more aligned (helpful, honest, and harmless) models and does “alignment science” to understand how alignment techniques work and try to extrapolate to uncover and address new failure modes.
  Reinforcement Learning – Reinforcement Learning is used by a variety of different teams, both for alignment and to teach models to be more capable at specific tasks.
  Platform – The Platform team builds shared infrastructure used by Anthropic's research and product teams. Areas of ownership include: the inference service that generates predictions from language models; extensive continuous integration and testing infrastructure; several very large supercomputing clusters and the associated tooling.
  Interpretability — The Interpretability team investigates what’s going on inside large language models — in a sense, they are trying to reverse engineer the concepts and mechanics from the inscrutable learned weights of these systems. Their goal is to ensure that AI systems are safe by being able to assess whether they’re doing what we actually want, all the way down to the individual neurons.
  Societal Impacts — Our Societal Impacts team designs and executes experiments that evaluate the capabilities and harms of the technologies we build. They also support the policy team with empirical evidence.
  Product — The Product research team trains, evaluates, and improves upon Claude, integrating all of our research techniques to make our AI systems as safe and helpful as possible.

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Software Engineer, London