Strategic Overview
Staff+ Software Engineer, Account Abuse (Machine Learning) at Anthropic is a aiml opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as 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,….
The role is listed for San Francisco, CA / New York City, NY with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
We want AI to be safe and beneficial for our users and for society as a whole.
Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
Key Responsibilities & Objectives
- 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
Required Qualifications & Candidate Blueprint
- 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)
Salary & Benefits
Published compensation: $320,000 – $485,000 USD
Salary Benchmark
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Benchmark uses active employer job postings in our database and excludes this listing. It is not a third-party salary estimate, and currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
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.
Key responsibilities
- 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
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 (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
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 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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Logistics
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.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. 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 think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This aiml opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly 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 — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
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