[Remote] Senior Engineering Manager, ML Platform

Note: The job is a remote job and is open to candidates in USA. Sift is an AI-powered fraud platform that uses machine learning and user identity data to secure digital trust for global businesses. The Senior Engineering Manager will lead and grow the ML platform team, overseeing systems that train, evaluate, and serve production models while driving technical strategy, automation, customer engagements, and cross-functional collaboration.

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

Skills

• • 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 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

Benefits

• Equity
• 401k plan
• Medical, dental and vision coverage
• Wellness reimbursement
• Education reimbursement
• Flexible time off

Company Overview

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