Staff+ Software Engineer (Account Abuse, Machine Learning)

• 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
• 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
• 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
• 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
• Train, evaluate, and deploy models that detect account-level abuse and fraud, running them both offline and online
• Build tooling that automates more of the model development lifecycle, including using Claude to speed up feature development, training, and evaluation
• Make backtesting, shadow deployment, and staged rollout the default path to production, with monitoring for training / serving skew, drift, and adversarial adaptation
• Work with our data scientists and our Policy & Enforcement team to improve label coverage and quality
• Partner with product and platform teams to gather signals and integrate model decisions with minimal impact on their systems’ latency, stability, or overall architecture

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office- Experience training machine learning models and deploying them to production
• Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders
• Working understanding of point-in-time correctness and training / serving skew, and how to prevent both
• Proficiency in Python and SQL
• Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow)
• Not all strong candidates will meet every single qualification as listed
• We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team
• 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 encourage you to apply even if you do not believe you meet every single qualification
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Experience building unsupervised, clustering-based or graph-based detection systems to surface coordinated account abuse
• Experience training ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking
• Experience with AutoML or other approaches to automating the ML workflow
• Experience with tree-based models on tabular data
• Experience with stream processing engines such as Flink, Beam / Dataflow, or Kafka Streams
• Care about the societal impacts of AI and want your work to make powerful systems safer
• Experience building or operating a feature platform such as Chronon, Feast, or Tecton
• Experience working with scarce, delayed, or noisy labels
• Experience in integrity, spam, fraud, or abuse detection