Senior Engineering Manager, ML Platform

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 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.

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.

Introduction interview: 30- 45 minutes with a recruiter to discuss your background and the role.

Hiring Manager interview: 30- 45 minutes with the hiring manager to explore your fit for the position.

Hybrid onsite loop with the team: approximately 4–5 hours covering system design, a technical deep dive, a cross-functional stakeholder scenario, and values & behavior.

At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.

This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy A little about us: Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn .