Senior Engineering Manager, ML Platform

Location: San Francisco, California or Seattle, Washington

Employment Type: Full time, Hybrid

The Machine Learning 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 are Sift's Data Science and ML Engineering team responsible to ship models fast, prove they work, and trust them in production.

We're hiring a Senior Engineering Manager to lead this team. 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.

• 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.

• 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.

• 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.

GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python

• 8+ years of overall hands-on engineering experience, including 4+ years managing software, data science or machine learning engineering teams.

• Experience managing Data Scientist and/or in-depth knowledge for data science.

• 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. or MS/Phd in Computer Science (or related technical discipline), or equivalent practical experience.

• Experience with large-scale distributed ML…