Staff+ Software Engineer, Account Abuse (Machine Learning)

Details

Anthropic's Account Abuse team ensures computing capacity is allocated fairly, minimizes resources available to bad actors, and prevents them from returning. This role focuses on building machine learning systems that detect and stop abuse at scale.

The position is for a full-stack machine learning engineer with experience across model training, productionization, and evaluation. The work involves classical machine learning on structured and behavioral data rather than deep learning or LLM internals. Candidates should have trained and shipped models in high-stakes production environments and care about robust production systems, measurement, precision, and safe rollout.

Responsibilities

• Build and operate a feature computation platform serving 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 to automate the model development lifecycle, including using Claude to accelerate feature development, training, and evaluation.
• Make backtesting, shadow deployment, and staged rollout the standard path to production.
• Monitor training and serving skew, model drift, and adversarial adaptation.
• Work with data scientists and the 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 system latency, stability, and architecture.

Requirements

Minimum Qualifications

• Proficiency in Python and SQL.
• Experience training machine learning models and deploying them to production.
• Experience building data pipelines with a batch processing engine such as Spark or Beam and a workflow scheduler such as Airflow.
• Working understanding of point-in-time correctness and training/serving skew, including how to prevent both.
• Strong communication skills and the ability to explain technical tradeoffs to non-technical stakeholders.
• Bachelor's degree or an equivalent combination of education, training, and experience.
• A field of study relevant to the role, as demonstrated through coursework, training, or professional experience.

Preferred Qualifications

• Experience building or operating a feature platform such as Chronon, Feast, or Tecton.
• Experience with stream processing engines such as Flink, Beam/Dataflow, or Kafka Streams.
• Experience training machine learning models in production with demanding serving requirements, such as fraud, risk, or ranking systems.
• Experience with tree-based models on tabular data.
• Experience building unsupervised, clustering-based, or graph-based detection systems for identifying coordinated account abuse.
• Experience in integrity, spam, fraud, or abuse detection.
• Experience working with scarce, delayed, or noisy labels.
• Experience with AutoML or other approaches to automating the machine learning workflow.
• Interest in the societal impacts of AI and in making powerful systems safer.

Compensation

The annual salary range is $320,000–$485,000 USD.

Benefits and Logistics

• Hybrid policy: Staff are currently expected to work from one of Anthropic's offices at least 25% of the time, although some roles may require more office time.
• Anthropic sponsors visas, although sponsorship may not be successful for every role or candidate. The company retains an immigration lawyer to provide assistance.
• Competitive compensation and benefits.
• Optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours.
• Office space for collaboration.