Senior Engineering Manager, ML Platform

Sift•,

12d•Hybrid

About The Position

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. 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.

Requirements

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.

Nice To Haves

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).
Familiarity with using AI coding assistants (e.g., Claude Code) to accelerate development.

Responsibilities

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.