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:

We are looking for software engineers to help build safety and oversight mechanisms for our AI systems. As a trust and safety software engineer, you will work to monitor models, prevent misuse, and ensure user well-being. This role will focus on building systems to detect unwanted model behaviors and prevent disallowed use of models. You will apply your technical skills to uphold our principles of safety, transparency, and oversight while enforcing our terms of service and acceptable use policies.

Responsibilities:

Develop monitoring systems to detect unwanted behaviors from our API partners and potentially take automated enforcement actions; surface these in internal dashboards to analysts for manual review
Build abuse detection mechanisms and infrastructure
Surface abuse patterns to our research teams to harden models at the training stage
Build robust and reliable multi-layered defenses for real-time improvement of safety mechanisms that work at scale
Analyze user reports of inappropriate content or accounts

You may be a good fit if you:

Bachelor’s degree in Computer Science, Software Engineering or comparable experience
3-8+ years of experience in a software engineering position, preferably with a focus on integrity, spam, fraud, or abuse detection.
Proficiency in SQL, Python, and data analysis tools.
Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Strong candidates may also:

Have experience building trust and safety mechanisms for AI/ML systems, such as fraud detection models or security monitoring tools or the infrastructure to support these systems at scale
Have experience with machine learning frameworks like Scikit-Learn, Tensorflow, or Pytorch, and experience building machine learning models
Have experience with prompt engineering, jailbreak attacks, and other adversarial inputs
Have worked closely with operational teams to build custom internal tooling

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

The expected salary range for this position is:

Annual Salary:

£185,000—£240,000 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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Why Anthropic? *

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Will you now or will you in the future require employment visa sponsorship to work in the country in which the job you're applying for is located? *

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, Trust & Safety (London)