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Signifyd is a fraud prevention and payment protection platform trusted by thousands of merchants across 100+ countries, processing billions of transactions annually. The Senior Machine Learning Engineer will join the ML team responsible for building, maintaining, and monitoring production ML models and offline experimentation frameworks that power Signifyd's core fraud detection capabilities.
In this role, you will own the end-to-end lifecycle of high-impact ML projects, from offline experimentation through deployment to production. You'll drive technical execution within the ML team, improving model performance, refining experimentation processes, and ensuring fraud detection systems are robust, scalable, and scientifically sound.
Key responsibilities include:
- Expanding ML capabilities by identifying, prototyping, and integrating new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability
- Enabling high-velocity experimentation by owning the design and implementation of ML pipeline components that accelerate innovation
- Collaborating across Product, Engineering, and Risk teams to translate business requirements into technical solutions
- Fostering a culture of technical excellence by championing best practices in testing, documentation, model monitoring, and development
You'll work closely with Platform Engineering teams to contribute novel modeling methods, advanced feature engineering, and robust statistical practices. The role requires strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale. You must have proven track record of taking ML projects from research/prototype to high-scale production environments.
Required qualifications: 4-6+ years of post-undergrad work experience in production-grade ML environments; degree in Computer Science, Statistics, or comparable quantitative field; proficiency in Python, SQL, key ML libraries, and Spark; strong communication skills for both technical and non-technical audiences; outcome-oriented mindset focused on business impact.
Nice-to-have: experience in fraud, fintech, payments, or e-commerce; passion for production-grade code; Master's or PhD.
