Jobs at Sift

Lead enterprise sales revenue and pipeline growth, manage complex SaaS sales cycles, enforce MEDDPICC and Command of the Message, coach Enterprise Account Executives, shape go-to-market strategy, and build executive relationships across customer and cross-functional stakeholders.

Lead and grow the ML Platform team, owning roadmap, execution, architecture, and production quality for model training, evaluation, serving, and monitoring systems. Partner with customers, Sales, Solutions Engineering, Data Science, Infrastructure, Product, and Customer Success on technical proof-of-value engagements. Modernize feature infrastructure, automate ML lifecycle processes, reduce technical debt, improve evaluation frameworks, and hire and mentor engineering talent.

Build and deploy low-latency, production-grade ML models and end-to-end pipelines for fraud detection. Engineer high-frequency time-series features from massive event streams, maintain automated MLOps/CI-CD infrastructure, optimize runtime scoring performance across distributed systems, and collaborate with infrastructure, product, and data science teams to translate fraud signals into robust algorithmic solutions.

Drive enterprise SaaS sales from prospecting through close, expanding adoption among strategic accounts. Engage business and technical decision-makers, including C-suite stakeholders, to address fraud challenges using Sift’s machine learning platform. Build consultative business cases, articulate platform differentiation, collaborate cross-functionally, and help shape sales strategy and growth. Requires strong organizational skills, adaptability, and experience managing complex B2B sales cycles.

Manage a portfolio of customer accounts, build trusted relationships, diagnose needs, drive adoption of Sift's platform, track business outcomes, advocate for customer feedback, support QBRs, and develop expertise in fraud, payments, trust and safety, and SaaS products.

Serve as the persistent technical partner for strategic and premium customers, diagnosing workflows, labeling, configurations, model behavior, and technical health. Build success plans with Customer Success Managers, guide implementations and model releases, conduct technical health reviews, coordinate fraud incident responses, and communicate technical findings to business and engineering stakeholders. Identify recurring customer patterns and provide feedback to Product and Engineering while supporting account retention and growth.