Jobs at Sift

Supports accounts payable, general ledger accounting, journal entries, balance sheet and bank reconciliations, fixed assets, month-end close, audits, compliance, and internal controls. The role also improves accounting processes, uses AI-assisted workflows, and partners with FP&A and Accounting teams on analysis and ad hoc requests. It requires 4–6 years of accounting experience, US GAAP knowledge, ERP and expense software experience, and strong Excel skills.

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.

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.

Manage end-to-end contracting for customer, vendor, and partner agreements: draft, negotiate, and close standard commercial contracts; review AI-generated redlines; own contract automation, clause libraries, intake, repository, renewals, and reporting; collaborate with Sales, Deal Desk, Finance, Security, and Product; escalate complex or high-risk issues to counsel.

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.