Staff+ Software Engineer, Account Abuse (Machine Learning)

San Francisco, United States; New York, United States

Apply on the company's site · job-boards.greenhouse.io

From $320k/year

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“Visa sponsorship: We do sponsor visas!”

H-1B sponsor: 201 approvals in FY2025–FY2026USCIS approved 201 new H-1B petitions (new hires and transfers from other employers) for this employer in FY2025–FY2026; the latest fiscal year may be partial. It doesn't guarantee this job offers sponsorship — confirm with the employer. A $100,000 payment on new H-1B petitions for workers outside the US (2025 proclamation, extended to September 2027) is blocked by federal courts as of October 2026; check the current rules with the employer.

Source: H-1B Employer Data Hub, U.S. Citizenship and Immigration Services (USCIS).

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🇺🇸 Working in United States

Possible country route: H-1B / other employer-petition routes. H-1B normally starts with an employer petition for a qualifying specialty occupation. Cap-subject cases also have registration and selection requirements; other routes differ.

Registration, petition and consular stages differ. Selection is not guaranteed and premium petition processing is not an end-to-end visa timeline.

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Confirm the intended route. Ask whether the employer will petition and whether the position is cap-exempt.
Check petition requirements. Use USCIS instructions for the applicable category and cap process.
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Reviewed 2026-10-14. Country routes are context, not a confirmation that this vacancy or your passport qualifies.

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Description (EN)

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.

Annual Salary:

$320,000—$485,000 USD

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.

Never pay a recruiter for a job offer or a visa promise. Check the original posting and employer identity before sharing documents. Pay any legitimate government visa fees only through official channels.

Published 21 days ago

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