Top Tech Jobs & Startup Jobs

Leads and develops an engineering team building, deploying, and operating machine-learning models and services for fraud prevention. Owns roadmap delivery, technical decisions, reliability, testing, monitoring, and stakeholder communication. Partners with data scientists, ML engineers, product, risk, and engineering teams. Drives responsible AI-tool adoption, recruits and coaches engineers, manages priorities, and ensures scalable, high-quality production systems.

Build and manage relationships with prospective and existing financial services partners, present tailored platform solutions, and represent Signifyd at industry and client events. Translate business needs into technical requirements and coordinate Product, Engineering, Legal, Sales, and partnerships teams. Manage client engagements, align internal milestones with external commitments, communicate roadmap timelines, shepherd contracts, identify risks, and drive resolution. Develop expertise in payments, e-commerce, fraud prevention, and the Signifyd platform.

Generate qualified pipeline for high-value e-commerce accounts through targeted research, personalized outreach, discovery calls, and collaboration with strategic sellers. Manage mid-funnel opportunities, maintain accurate CRM records, coordinate with Marketing, Sales Operations, and Partnerships, report on sales metrics, and contribute to sales process improvements. The role requires strong communication, organization, CRM proficiency, and understanding of longer sales cycles.

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.

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.

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.

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.