Software Engineer, Account Abuse (Machine Learning)

The shape of it

In the posting’s own words

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.

What it asks for · 5

• Proficiency in Python and SQL
• Experience training machine learning models and deploying them to production
• Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow)
• Working understanding of point-in-time correctness and training / serving skew, and how to prevent both
• Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders

Also a plus

• 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 ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking
• Experience with tree-based models on tabular data
• Experience building unsupervised, clustering-based or graph-based detection systems to surface 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 ML workflow

What the job covers

• 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

Tools and skills named

Words the posting leans on

• experience11×
• models11×
• training8×
• abuse5×
• data5×
• systems5×
• production4×
• build3×
• experience building3×
• feature3×
• fraud3×
• machine learning3×
• platform3×
• account abuse2×
• beam2×
• care2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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