Pay
Higher than most similar jobs
$320K–$485K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Staff+ Software Engineer, Account Abuse (Machine Learning)
San Francisco, CA | New York City, NY
Live, first seen by us 21 days ago- Pay
USD 320,000–485,000/year — pay source
Annual Salary: $320,000—$485,000 USD Logistics
The annual compensation range for this role is listed below.
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
Before you apply
- Work authorization
- Sponsors visas
What you’ll work on
Full postingThe 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.
We're looking for full stack machine learning engineers with experience across model training, productionization, and evaluation.
Build and operate a feature platform for both model training and real-time scoring
Train, evaluate, and deploy models that detect account-level abuse and fraud
Build tooling that automates model development, including using Claude to speed it up
Make backtesting, shadow deployment, and staged rollout the default path to production
Work with data scientists and the Policy & Enforcement team to improve label quality
From the employer’s posting
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.
Build and operate a feature computation platform that serves both model training and real-time scoring
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
Work with our data scientists and our Policy & Enforcement team to improve label coverage and quality
What you’ll bring
All qualificationsCore experience
- 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)
- Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders
Preferred experience
- 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
Qualification wording
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)
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
Education & alternatives
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
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 14, 202621 days ago
- Recorded sightings
- 105
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 21
Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Anthropic is hiring
27 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Kafka
- Airflow
Where the posting names them
Minimum qualifications - Proficiency in Python and SQL - Experience training machine learning models and deploying them to production
- 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 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
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Source details
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Annual Salary: $320,000—$485,000 USD Logistics
More source context
The annual compensation range for this role is listed below.
- Location & working pattern
San Francisco, CA | New York City, NY
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.
- Work authorization
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.