> Markdown version of [/jobs/ext/3136290-full-stack-machine-learning-engineers](https://www.wearedevelopers.com/jobs/ext/3136290-full-stack-machine-learning-engineers). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # full stack machine learning engineers - **Company:** Anthropic's Mission - **Location:** New York, United States - **Salary:** $320,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Airflow, Batch Processing, Data Flow Control, Python (Programming Language), Machine Learning, Standard Sql, Large Language Models, Apache Spark, Deep Learning, Low Latency, Apache Flink, Apache Kafka, Machine Learning Operations, Data Pipelines - **Published:** September 29, 2026 - **Apply:** https://startup.jobs/staff-software-engineer-account-abuse-machine-learning-anthropic-3-10221189 ## About the Role * 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, * 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 * Care about the societal impacts of AI and want your work to make powerful systems safer, Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. ## Description 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 ## Related Videos - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)