About the role
At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy.
Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here!
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.
Our Culture
We value tenacity, curiosity, and a hunger for learning. Our adversaries are highly motivated fraudsters looking to exploit any gap. We seek equally motivated individuals who are passionate about keeping our customers safe while pulling the field of adversarial machine learning forward.
The Role
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.
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.
Requirements:
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'
Nice to have:
Previous experience in fraud, fintech, payments, or e-commerce.
Passion for writing well-tested production-grade code
A Master’s Degree or PhD.
Why Join Us?
Make an Impact – Your work will directly shape the future of fraud prevention, protecting billions of payments.
Lead & Grow – Drive high-visibility initiatives and develop leadership skills in a fast-paced, high-growth environment.
Innovate at Scale – Work with cutting-edge ML technologies and experiment freely to push the boundaries of what’s possible.
Collaborative Culture – Join a team that values curiosity, ownership, and continuous learning.
#LI-Remote
Benefits in our US offices:
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.
We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
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