Senior Engineering Manager, ML Platform

Location: San Francisco, California or Seattle, Washington

Employment Type: Full time, Hybrid

About the Team

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.

What We're Looking For

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.

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.

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, data science or machine learning engineering teams.