[All remote jobs](https://jobicy.com/jobs.md)[![Signifyd logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/9be4f098-221.jpg)](https://jobicy.com/company/signifyd.md)[Signifyd](https://jobicy.com/company/signifyd.md) # Senior Machine Learning Engineer I // II Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/signifyd.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished25 Oct 2026Apply before24 Nov 2026Listing views34Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Signifyd is hiring a Senior Machine Learning Engineer to build and operate production machine-learning systems for fraud detection. The role owns the full ML lifecycle, including offline experimentation, feature engineering, deployment, monitoring, and evaluation of high-scale models. It requires strong Python, SQL, Spark, statistical evaluation, and supervised and unsupervised learning experience. The engineer will collaborate with product, platform engineering, and risk teams to translate fraud-prevention needs into reliable technical solutions. Experience in fraud, fintech, payments, or e-commerce and a commitment to tested production code are advantageous. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, high-impact role requiring end-to-end ownership of production ML systems that make fraud decisions at very large scale. Success requires deep ML and statistical expertise alongside reliable engineering execution in an adversarial domain. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$150k–$205k0$225.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer explicitly lists annual US base-salary ranges of $140,000–$190,000, varying by pay zone; the midpoint across the full disclosed range is $165,000 per year. A reasonable estimated US market range for a senior machine learning engineer focused on production fraud systems is $150,000–$205,000 annually. Equity and an annual performance bonus of up to 10% are additional compensation and are not included in base salary. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Fraud Detection](https://jobicy.com/jobs?search_keywords=Fraud%20Detection.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Apache Spark](https://jobicy.com/jobs?search_keywords=Apache%20Spark.md)[Statistical Evaluation](https://jobicy.com/jobs?search_keywords=Statistical%20Evaluation.md)[Supervised Learning](https://jobicy.com/jobs?search_keywords=Supervised%20Learning.md)[Unsupervised Learning](https://jobicy.com/jobs?search_keywords=Unsupervised%20Learning.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[Model Monitoring](https://jobicy.com/jobs?search_keywords=Model%20Monitoring.md)[ML Pipelines](https://jobicy.com/jobs?search_keywords=ML%20Pipelines.md)[Production ML](https://jobicy.com/jobs?search_keywords=Production%20ML.md)[Experimentation](https://jobicy.com/jobs?search_keywords=Experimentation.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Model Deployment](https://jobicy.com/jobs?search_keywords=Model%20Deployment.md) Sample interview questionsDescribe a production machine-learning model you took from experimentation to deployment.I would explain the business objective, data and feature design, offline evaluation criteria, deployment architecture, monitoring plan, and the measurable outcome. I would also highlight how I handled reproducibility, testing, rollback procedures, and model drift after launch. How would you evaluate a fraud model when fraud labels are delayed and class imbalance is severe? I would use time-based validation, precision-recall metrics, recall at a fixed review or loss budget, calibration checks, and cost-sensitive evaluation rather than accuracy. I would account for delayed labels through maturity windows and monitor leading indicators, such as score distributions and approval-rate changes, until outcomes mature. How do you design an ML experimentation framework that enables high-velocity iteration without compromising reliability? I would standardize data and feature versioning, reusable training and evaluation components, automated tests, experiment tracking, and clear promotion gates. This lets engineers compare experiments consistently while requiring validation, reproducibility, and monitoring readiness before production release. What approaches would you use to detect and respond to model drift in a fraud system? I would monitor feature distributions, prediction distributions, calibration, delayed performance metrics, and operational business metrics by relevant customer and traffic segments. When drift exceeds agreed thresholds, I would investigate upstream changes and adversarial behavior, then use a controlled rollback, retraining, threshold adjustment, or targeted feature update as appropriate. How do you communicate a complex model change to Product and Risk stakeholders? I would begin with the decision being improved and its expected business impact, such as fraud-loss reduction, approval lift, or review-volume change. I would present key trade-offs, confidence intervals, rollout safeguards, and monitoring metrics in plain language, while providing technical documentation for engineering partners. Opportunity details ## About this 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](https://www.signifyd.com/about/#values)! 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. [Signifyd’s Applicant Privacy Notice](https://drive.google.com/file/d/1HSnMY6HGjB1FNRX4Ez9bUre5mrPPcHIq/view?usp=sharing) Show more [Apply now >](https://jobicy.com/jobs/156893-senior-machine-learning-engineer-i-ii.md) > Annual salary information is not provided for this position. 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