Senior Machine Learning Engineer I / II

signifyd95; United States; Remote; USD 130,000 - 200,000; Full time

Benefits offered by this job

Remote work

Health insurance

Dental coverage

Vision coverage

Retirement plan matching

Stock options

Performance bonus

Parental leave

Paid time off

Disability insurance

Life insurance

Learning budget

Job summary

signifyd95 seeks a production-ready ML engineer to own technical delivery for fraud-detection ML projects. You will work on offline experimentation, model and pipeline development, deployment, and monitoring, aiming to improve performance while keeping systems scalable and scientifically sound.

You will collaborate with product, engineering, and risk teams to translate business needs into robust modeling solutions, while promoting strong practices in testing, documentation, and model monitoring.

Qualifications

• Degree in computer science, statistics, or a comparable quantitative field.
• 4–6 years of post‑degree experience in production ML.
• Strong grounding in ML theory and statistical evaluation.
• Track record of moving ML work into high‑scale production systems.
• Proficiency with Python, SQL, major ML libraries, and Spark.
• Ability to explain technical findings to both technical and non‑technical stakeholders.

Responsibilities

• Identify, prototype, and integrate modeling methods and infrastructure that improve fraud detection and scale.
• Design and build ML pipeline components to speed up experimentation and reliability.
• Lead projects from research or prototype through deployment and production operation.
• Evaluate model performance and strengthen experimentation practices with sound statistics.
• Collaborate with product, engineering, and risk teams to translate business needs into technical work.
• Promote strong testing, documentation, model monitoring, and software development practices.

Skills

ML concepts

Statistical evaluation

Stakeholder communication

Production mindset

Education

Degree in CS/Statistics/Quantitative field

Tools

Python

SQL

Spark

Job description

Role overview

Own technical delivery for production machine learning projects that support fraud detection in online commerce. The work spans offline experimentation, model and pipeline development, deployment, and ongoing monitoring, with a focus on improving performance while keeping systems scalable and evaluation scientifically sound. You’ll partner with product, engineering, and risk colleagues to turn business needs into robust modeling solutions.

Responsibilities

• Identify, prototype, and integrate modeling methods and infrastructure that improve fraud detection and scale.
• Design and build machine learning pipeline components that make experimentation faster and more reliable.
• Take high-impact projects from research or prototype through deployment and production operation.
• Evaluate model performance and strengthen experimentation practices using sound statistical methods.
• Work with product, engineering, and risk teams to translate customer and business needs into technical work.
• Promote strong practices in testing, documentation, model monitoring, and software development.

Requirements

• Degree in computer science, statistics, or a comparable quantitative field.
• At least four to six years of post‑degree experience in a production machine learning environment.
• Strong grounding in machine learning theory and statistical evaluation, including supervised or unsupervised methods at scale.
• Track record of moving machine learning work into high‑scale production systems.
• Proficiency with Python, SQL, major machine learning libraries, and Spark.
• Ability to explain technical findings to both technical colleagues and non‑technical stakeholders, with attention to business outcomes.

Nice to have

• Experience in fraud prevention, financial technology, payments, or e-commerce.
• Interest in writing well‑tested production code.
• Master´s degree or doctorate.

Benefits and work setup

• The role is listed as remote. U.S. benefits include health, dental, and vision coverage; retirement plan matching; stock options; performance bonus eligibility; paid parental leave; paid time off; disability and life insurance; and a learning budget.