Staff+ Software Engineer, Account Abuse (Machine Learning)

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2><strong>About the role</strong></h2> <p>The Account Abuse team is tasked with ensuring Anthropic's computing capacity is allocated fairly, minimizing resources available to bad actors and preventing them from coming back. As a software engineer on this team, you will build the machine learning systems that help us detect and stop abuse at scale. The ideal candidate can see things from opponents' perspectives, understand their means and motives, and anticipate their responses to countermeasures.</p> <p>We're looking for full stack machine learning engineers with experience across model training, productionization, and evaluation. You'll also look for ways to use Claude to speed up how these models get built and maintained.</p> <p>This is classical ML on structured and behavioral data. You do not need a deep learning background or knowledge of LLM internals. What matters is that you have trained and shipped models where the stakes are real, and that you care about building robust production systems as much as the model itself. A false positive here is a legitimate customer locked out, so measurement, precision, and safe rollout are part of the job.</p> <h2><strong>Key responsibilities</strong></h2> <ul> <li>Build and operate a feature computation platform that serves both model training and real-time scoring, with point-in-time correct training data and low-latency online retrieval</li> <li>Train, evaluate, and deploy models that detect account-level abuse and fraud, running them both offline and online</li> <li>Build tooling that automates more of the model development lifecycle, including using Claude to speed up feature development, training, and evaluation</li> <li>Make backtesting, shadow deployment, and staged rollout the default path to production, with monitoring for training / serving skew, drift, and adversarial adaptation</li> <li>Work with our data scientists and our Policy & Enforcement team to improve label coverage and quality</li> <li>Partner with product and platform teams to gather signals and integrate model decisions with minimal impact on their systems' latency, stability, or overall architecture</li> </ul> <h2><strong>Minimum qualifications</strong></h2> <ul> <li>Proficiency in Python and SQL</li> <li>Experience training machine learning models and deploying them to production</li> <li>Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow)</li> <li>Working understanding of point-in-time correctness and training / serving skew, and how to prevent both</li> <li>Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders</li> </ul> <h2><strong>Preferred qualifications</strong></h2> <ul> <li>Experience building or operating a feature platform such as Chronon, Feast, or Tecton</li> <li>Experience with stream processing engines such as Flink, Beam / Dataflow, or Kafka Streams</li> <li>Experience training ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking</li> <li>Experience with tree-based models on tabular data</li> <li>Experience building unsupervised, clust