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Sift is hiring a Senior Engineering Manager to lead the Machine Learning Platform team (internally "Potato Radius"), which builds training pipelines, feature infrastructure, and evaluation systems powering Sift's fraud detection and risk scoring across 700+ customers and over a trillion events annually.
You will own the roadmap, execution, and quality of systems that train, evaluate, and serve ML models in production. This is a backfill role with significant opportunity to modernize a foundational platform at a critical moment—Sift's competitive advantage increasingly depends on winning proof-of-value engagements quickly, and this team's tooling is central to that speed.
Key responsibilities include: leading and growing a team of ML platform engineers and data scientists; staying deeply technical by reviewing designs, unblocking engineers on hard problems, and making credible architectural calls; driving customer POVs by partnering directly with strategic customers and Sales/Solutions Engineering; reducing technical debt across the ML platform while maintaining feature velocity; building and maturing evaluation frameworks that give Data Science and ML Engineering fast, trustworthy signals on model quality; automating the ML lifecycle (training, evaluation, deployment, monitoring); and aligning platform investments cross-functionally with Data Science, Core Infrastructure, Product, and Customer Success.
Example projects include launching a unified model evaluation framework for fast apples-to-apples comparisons before production, evolving core feature infrastructure including a new global feature store, building tooling for faster customer proof-of-value engagements, introducing agentic AI-assisted tooling for customer investigations, and building automation that detects active fraud attacks and adjusts score calibration in real time.
You bring 8+ years of hands-on engineering experience (including 4+ years managing software or ML engineering teams), deep technical fluency in ML systems at production scale, a proven track record leading technical customer engagements, demonstrated success reducing technical debt in live high-traffic systems, experience designing or scaling evaluation frameworks, a track record of identifying and automating manual engineering processes, and strong hiring and mentoring skills. A B.S. in Computer Science or equivalent practical experience is required.
Bonus qualifications include experience with large-scale distributed ML infrastructure (Spark, Flink, Databricks), fraud detection or trust & safety domains, hands-on GCP or AWS ML infrastructure experience, streaming architectures (Kafka), containerized deployments (Docker, Kubernetes), and familiarity with AI coding assistants.
Technical stack: GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python.
