Senior Machine Learning Engineer

Hidden Jobs; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Retirement matching

Paid leave

Holidays

Flexible vacation

Parental leave

Company-provided technology

Job summary

Step is seeking a senior ML engineer to design, deploy, and operate fraud and risk ML systems. The role is remote-first across the United States, with an office option in Palo Alto, CA. You will collaborate with operations and data teams to respond to evolving threats and performance signals.

You will lead model development, evaluation, and production deployment, and translate complex results into clear guidance for technical and non-technical stakeholders.

Qualifications

• At least five years of experience in data science or machine learning engineering.
• Strong proficiency with Python and SQL.
• Demonstrated experience developing and deploying machine learning models.
• Excellent data analysis and problem-solving skills.
• Ability to explain technical findings to technical and non-technical audiences.

Responsibilities

• Design, train, evaluate, and deploy machine learning models for risk assessment and fraud detection.
• Provide technical direction for risk and fraud initiatives and help shape the team’s broader strategy.
• Use SQL to retrieve, transform, and prepare data for analysis and model development.
• Write reliable, maintainable, production-grade code for machine learning systems.
• Apply statistical methods to design experiments, estimate sample sizes, and assess model performance.
• Partner with operations teams to investigate and respond to rapidly evolving fraud and risk events.

Skills

Python

SQL

Data science

Machine learning

Model deployment

Communication

Job description

Role overview

This role focuses on developing and deploying machine learning solutions that help detect fraud, manage risk, and reduce financial losses. You will serve as a senior technical contributor in the risk and fraud domain, combining data analysis, statistical experimentation, production engineering, and close collaboration with operations teams to respond to changing threats.

Responsibilities

• Design, train, evaluate, and deploy machine learning models for risk assessment and fraud detection.
• Provide technical direction for risk and fraud initiatives and help shape the team’s broader strategy.
• Use SQL to retrieve, transform, and prepare data for analysis and model development.
• Write reliable, maintainable, production-grade code for machine learning systems.
• Apply statistical methods to design experiments, estimate sample sizes, and assess model performance.
• Partner with operations teams to investigate and respond to rapidly evolving fraud and risk events.

Requirements

• At least five years of experience in data science or machine learning engineering.
• Strong proficiency with Python and SQL.
• Demonstrated experience developing and deploying machine learning models.
• Excellent data analysis and problem-solving skills.
• Ability to explain technical findings and decisions clearly to both technical and non-technical audiences.

Nice to have

• Experience with financial systems, lending, risk management, or fraud detection.

Benefits and work setup

• Remote-first position available within the United States, with an office option in Palo Alto, California.
• The source describes health, dental, vision, life insurance, retirement matching, paid leave, holidays, flexible vacation, parental leave, and company-provided technology.