Senior Machine Learning Engineer I // II

Role Description

The Senior Machine Learning Engineer will join our ML team. This team is responsible for building, maintaining, and monitoring the production ML models and offline experimentation frameworks that are at the core of Signifyd’s product. This includes the core fraud detection model that decides the majority of our traffic, alongside our model training and evaluation infrastructure. We work closely with Platform Engineering teams to contribute novel modeling methods, advanced feature engineering, and robust statistical practices.

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
• Ensuring our fraud detection systems are robust, scalable, and scientifically sound

Responsibilities:

• 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.

Qualifications

• A degree in Computer Science, Statistics, or a comparable quantitative field.
• 4-6+ years of post-undergrad work experience in a production-grade ML environment.
• Strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale.
• Proven track record of taking ML projects from research/prototype to high-scale production environments.
• Ability to communicate technical findings clearly to both technical peers and non-technical stakeholders.
• Proficiency in Python, SQL, key ML libraries, and Spark.
• 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.

Requirements

• Previous experience in fraud, fintech, payments, or e-commerce (nice to have).
• Passion for writing well-tested production-grade code (nice to have).
• A Master’s Degree or PhD (nice to have).

Benefits

• Discretionary Time Off Policy (Unlimited!)
• 401K Match
• Stock Options
• Annual Performance Bonus or Commissions
• Paid Parental Leave (12 weeks)
• On-Demand Therapy for all employees & their dependents
• Dedicated learning budget through Learnerbly
• Health Insurance
• Dental Insurance
• Vision Insurance
• Flexible Spending Account (FSA)
• Short Term and Long Term Disability Insurance
• Life Insurance
• Company Social Events
• Signifyd Swag

Compensation

In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions.

• Base Salary Ranges by Pay Zone:
• Tier 1 (NYC/SF Bay Area/Seattle): $160,000 - $190,000 annually
• Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $150,000 - $180,000 annually
• Tier 3 (US - All Other): $140,000 - $170,000 annually
• Equity: This role is eligible for a stock option grant of 4,000 stock options, based on the position level and internal compensation guidelines.
• Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary.