Staff Machine Learning Engineer, Radar

at Stripe

📍 United States

USD 253,500-380,300 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Data Pipelines @ 4 Data Science Deep Learning @ 4 FinTech @ 4 Fraud @ 4 LLM Machine Learning @ 7 Mathematics @ 6 NLP @ 4 Payments PyTorch @ 6 Spark @ 6 Statistics @ 6 TensorFlow @ 6 XGBoost @ 6

Details

The Radar ML team builds fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns more than 10 real-time deep learning models that must constantly evolve to stay ahead of fraudsters. These models also power the Radar product suite, which businesses use to screen payments and manage fraud.

The team is building new products, including defenses against AI token theft, free trial abuse, and programmatic attacks. This role owns machine learning work across the full lifecycle, from researching new fraud patterns and building and deploying models to sharing results directly with Stripe customers.

Responsibilities

Design, build, train, evaluate, deploy, and own production machine learning models that detect fraud across Stripe's global payments network.
Design and build large-scale machine learning systems that operate on diverse and large-scale data.
Experiment and iterate on machine learning models to achieve business goals related to data quality and accuracy.
Develop pipelines and automated processes to train and evaluate models in offline and online environments.
Integrate machine learning models into production systems and ensure their scalability and reliability.
Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where machine learning can improve outcomes for merchants and consumers.
Engage with the latest machine learning and artificial intelligence developments and transform innovative ideas into productionized solutions.
Mentor engineers and contribute to a strong machine learning engineering culture.

Requirements

Minimum Requirements

10+ years of industry experience building and shipping machine learning systems in production.
Proficiency with machine learning libraries and frameworks such as PyTorch, TensorFlow, XGBoost, and Spark.
Hands-on experience designing, training, and evaluating machine learning models.
Hands-on experience productionizing and deploying models at scale.
Hands-on experience orchestrating data pipelines and efficiently leveraging large-scale datasets.
Strong collaboration skills and the ability to work across teams and contribute to peers' success.
Ability to thrive with a high level of autonomy and responsibility, along with an entrepreneurial mindset.

Preferred Qualifications

Master's or PhD degree in machine learning, artificial intelligence, or a related field such as mathematics, physics, statistics, or computer science.
Experience in fintech, open banking, or financial data domains.
Experience with NLP, LLMs, or text classification at scale.
Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality.
Proven track record of building and deploying machine learning systems that have solved ambiguous business problems.
Experience with deep learning architectures, including transformers.