Jobs at Signifyd

Own strategic enterprise customer relationships from onboarding through growth, ensuring platform performance, alignment, retention, and expansion. Build executive stakeholder relationships, lead discovery and change-management sessions, resolve issues cross-functionally, create long-term growth plans, conduct business reviews, identify upsell and cross-sell opportunities, and collaborate with Product, Marketing, Sales, Implementation, and Risk teams. Travel approximately 30% and host executive customer engagements.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

The Chargebacks Analyst will conduct research on credit card chargebacks, minimize losses, review merchant requests, and support merchants in compliance with company policies. They will perform outreach, collaborate with cross-functional teams, and ensure accurate claim processing.

Lead design, implementation, and operation of Signifyd's GCP/Kubernetes cloud platform. Architect scalable, secure infrastructure, embed AI-driven automation, own SLOs and incident response, improve developer experience via CI/CD/GitOps tooling, mentor engineers, manage cloud capacity/FinOps, and build observability and security into the platform.

Supports recruiting by coordinating complex interviews, liaising with candidates and interviewers, resolving scheduling conflicts, collecting feedback, and sourcing candidates on LinkedIn. Also assists with HR reporting, analytics, compliance audits, digital file migration, employee experience initiatives, milestone management, and office administration. This is a 3–4 month remote contract role with a primary recruiting focus.

Lead and grow a machine learning engineering team responsible for fraud and risk models. Manage experimentation portfolios, balance research with delivery, evaluate model performance rigorously, and guide work through production release. Partner with Risk, platform, and infrastructure teams to set priorities, improve training and experimentation systems, communicate results, and make autonomous roadmap decisions. The role is a hands-on player-coach requiring technical depth, people leadership, strong communication, and comfort operating under uncertainty.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

The Machine Learning Engineer II builds production ML models to detect fraud, collaborates on engineering teams, and improves the commerce protection platform.

As a Senior Solutions Architect, you will design API solutions for strategic merchants, lead technical presentations, and collaborate with internal teams to address fraud and data challenges.

The role involves developing sales strategies, managing sales cycles, collaborating with teams, and establishing partnerships with large brands in e-commerce. It requires a consultative approach to address client needs and achieve revenue goals.

Analyze chargeback and transaction data to uncover trends, optimize dispute strategies, build dashboards, ensure metric integrity, support product launches, and collaborate cross-functionally to reduce losses and improve dispute outcomes.

Lead design and delivery of the public-facing API platform and ingestion services. Build scalable, reliable real-time distributed systems, collaborate with product and data teams, review designs and code, address technical debt, take operational ownership using observability tools, and mentor engineers.