Staff+ Software Engineer (Account Abuse, Machine Learning)

Join Anthropic, a leading AI safety and research company, as a Staff+ Software Engineer on the Account Abuse team. In this role, you will build machine learning systems to detect and prevent account abuse at scale. You will work with structured and behavioral data, and your contributions will have a direct impact on the safety and integrity of AI systems.

- Construire et exploiter une plateforme de calcul de fonctionnalités qui sert à la fois à l'entraînement des modèles et à la notation en temps réel. - Former, évaluer et déployer des modèles qui détectent les abus et la fraude au niveau des comptes, en les exécutant à la fois hors ligne et en ligne. - Collaborer avec les équipes de science des données et de politique et d'application pour améliorer la couverture et la qualité des étiquettes.

- 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