Senior ML Engineer — Production Fraud (Remote)

Jobicy; United States; Teletrabalho; USD 150 000 - 180 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

401K Match

Stock Options

Annual Bonus

Parental Leave

Health Insurance

Dental Insurance

Vision Insurance

FSA

Unlimited PTO

Therapy Access

Learning Budget

Life Insurance

Company Social Events

Resumo da oferta

Signifyd is hiring a Senior Machine Learning Engineer to build, operate, and scale production ML systems for fraud detection. The role owns the full ML lifecycle—from offline experimentation to deployment and monitoring of high-scale models.

Strong Python, SQL, Spark, and statistical evaluation are required; collaboration with product, platform engineering, and risk teams is essential. Experience in fraud, fintech, payments, or e-commerce is advantageous.

Qualificações

• 4-6+ years of post-undergrad work in production-grade ML.
• Experience building production ML systems for fraud or fintech is preferred.
• Strong Python/SQL/Spark and statistical evaluation skills.

Responsabilidades

• Expand ML capabilities with new technologies and infrastructure.
• Own high-velocity ML experimentation and pipeline components.
• Collaborate with Product, Engineering, and Risk to translate requirements into ML solutions.
• Maintain rigorous testing, documentation, and model monitoring practices.

Conhecimentos

Machine Learning

Fraud Detection

Python

SQL

Apache Spark

Statistical Evaluation

Supervised Learning

Unsupervised Learning

Feature Engineering

Model Monitoring

ML Pipelines

Production ML

Experimentation

Data Engineering

Model Deployment

Formação académica

Bachelor's degree in Computer Science, Statistics, or quantitative field

Advanced degree (optional)

Ferramentas

Python

SQL

Apache Spark

Descrição da oferta de emprego

Signifyd is hiring a Senior Machine Learning Engineer to build, operate, and scale production ML systems for fraud detection. The role owns the full ML lifecycle—from offline experimentation to deployment and monitoring of high-scale models.

Strong Python, SQL, Spark, and statistical evaluation are required; collaboration with product, platform engineering, and risk teams is essential. Experience in fraud, fintech, payments, or e-commerce is advantageous.