Staff+ Software Engineer, Account Abuse (Machine Learning)

United States Digital Space LLC; San Francisco, New York (CA, NY); Hybrid; USD 320,000 - 485,000; Full time

Job summary

United States Digital Space LLC is seeking a software engineer for the Account Abuse team to design and deploy ML systems that detect and stop abuse at scale. You will build a feature computation platform, train and evaluate models, and automate the ML lifecycle with Claude to accelerate development.

You will work with data scientists and product teams to integrate model decisions while maintaining low latency and robust safety in production.

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.

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

Skills

Python

SQL

ML training

Model evaluation

Tools

Spark

Airflow

Beam

Job description

About the company

the company’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 the company'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

Compensation

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.

How we're different

We believe that the highest-impact AI research will be big science. At the company 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.

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.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our