Senior Machine Learning Engineer I // II

USAfull timeRemote

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senior

$160k–$190k

H1B: no

Signifyd Senior Machine Learning Engineer I/II at Signifyd to own end-to-end lifecycle of high-impact ML projects including offline experimentation, deployment, and production. Improve model performance and ensure fraud detection systems are robust and scalable.

Responsibilities

As a Senior Machine Learning Engineer, you will be a driver of technical execution within the ML team. You won’t just build models—you’ll own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to deployment to production. You will be responsible for improving model performance, refining our experimentation processes, and ensuring our fraud detection systems are robust, scalable, and scientifically sound.
Expand ML Capabilities – Identify, prototype, and integrate new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability.
Enable High-Velocity Experimentation – Own the design and implementation of ML pipeline components that accelerate our innovation
Collaborate Across Functions – Partner with Product, Engineering, and Risk teams to translate business requirements into technical solutions and ensure ML initiatives align with customer needs.
Raise the Bar – Foster a culture of technical excellence by championing best practices in testing, documentation, model monitoring, and development.

Qualification

EducationExperienceTechnical DepthExecution FocusCommunicationTech StackMindsetPrevious experience in fraudA Master’s Degree or PhD

Required

Education: A degree in Computer Science, Statistics, or a comparable quantitative field.
Experience: 4-6+ years of post-undergrad work experience in a production-grade ML environment.
Technical Depth: Strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale.
Execution Focus: Proven track record of taking ML projects from research/prototype to high-scale production environments.
Communication: Ability to communicate technical findings clearly to both technical peers and non-technical stakeholders.
Tech Stack: Proficiency in Python, SQL, key ML libraries, and Spark
Mindset: A strong outcome-oriented mindset—you care about the "why" behind the models and the business impact they create.
Attention to detail is critical in fraud prevention. To demonstrate this, please start your response to the first application question with the word 'Stochastic'