Senior Engineering Manager, ML Platform

Location: San Francisco, California or Seattle, Washington Employment Type: Full time Location Type: Hybrid Department: Engineering About the Team The Machine Learning Platform team — internally known as "Potato Radius" — builds the training pipelines, feature infrastructure, and evaluation systems behind every score Sift returns, across more than 700 customers and a trillion-plus events a year. We give Sift's Data Science and ML Engineering teams the tooling to ship models fast, prove they work, and trust them in production. What We're Looking For We're hiring a Senior Engineering Manager to lead this team as a backfill for our outgoing lead. This isn't a maintenance role — it's a chance to modernize a foundational platform at a moment when the stakes are high: our biggest deals increasingly come down to who can win a competitive proof-of-value the fastest, and this team's tooling determines whether we win it. You're a manager who's inspiring and technical, and who knows how to bring focus to what matters now without losing sight of the long term. You value collaboration and transparency, operate with a get-stuff-done mindset, and bring the technical depth and bias for shipping to spot the manual, brittle, or duplicated work that's quietly slowing the team down. You build a culture of mentorship, give regular and constructive feedback, set clear goals, and grow your team by hiring effectively. Projects You Might Lead

• Launch a unified model evaluation framework that gives Data Science fast, trustworthy, apples-to-apples comparisons before a model ever reaches production or shadow traffic.
• Evolve core feature infrastructure — including a new global feature store — to improve accuracy and unlock faster experimentation.
• Build the tooling and metrics that let Sift run faster, sharper customer proof-of-value engagements, online and offline, so we win competitive bake-offs instead of losing them to slow iteration.
• Bring a fresh approach to model configuration, replacing tribal knowledge and manual gating with auditable, safely-controlled releases.
• Introduce agentic, AI-assisted tooling into customer investigations, automating repetitive data pulls and validation so analysts spend their time on judgment calls, not manual digging.
• Build automation that detects an active fraud attack, adjusts score calibration in real time, and cleanly reverts once it subsides. What You'll Do
• Lead and grow the team: Own the roadmap, execution, and quality of the systems that train, evaluate, and serve Sift's ML models in production, leading a team of ML platform engineers and data scientists.
• Stay technical: Review designs, unblock engineers on hard problems, and make credible calls on architecture and trade-offs.
• Drive customer POVs: Partner directly with strategic customers and Sales/Solutions Engineering on technical proof-of-value engagements, translating customer requirements into platform capabilities.
• Reduce technical debt: Drive a sustained, measurable reduction in technical debt across the ML platform, balancing new feature delivery with the health of existing systems.
• Build evaluation frameworks: Mature the systems that give Data Science and ML Engineering fast, trustworthy signals on model quality before and after deployment.
• Automate the ML lifecycle: Identify repeatable, manual processes across training, evaluation, deployment, and monitoring, and drive their automation.
• Partner cross-functionally: Align platform investments with business priorities alongside Data Science, Core Infrastructure, Product, and Customer Success. Technical Stack GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python What Would Make You a Strong Fit
• 8+ years of overall hands-on engineering experience, including 4+ years managing software or machine learning engineering teams.
• Deep technical fluency in machine learning systems: model training pipelines, feature engineering, model serving, and evaluation at production scale.
• Proven track record leading technical customer engagements or POVs, including direct interaction with enterprise customers.
• Demonstrated success reducing technical debt in a live, high-traffic production system without stalling feature delivery.
• Experience designing or scaling evaluation frameworks (offline and/or online) for machine learning models.
• Track record of identifying manual, repeatable engineering processes and driving their automation.
• Experience hiring, mentoring, and developing engineering talent.
• B.S. in Computer Science (or related technical discipline), or equivalent practical experience. Bonus Points
• Experience with large-scale distributed ML infrastructure such as Spark, Flink, Databricks, or similar.
• Familiarity with fraud detection, risk, or trust & safety domains.
• Hands-on experience with GCP or AWS ML infrastructure.
• Experience with streaming architectures (e.g., Kafka) and containerized/orchestrated deployments (Docker, Kubernetes).
• Familiari