Senior Engineering Manager (ML Platform)

• 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
• Technical Stack: GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python

Benefits

• Competitive total compensation package
• 401k plan
• Medical, dental and vision coverage
• Wellness reimbursement
• Education reimbursement
• Flexible time off- Track record of identifying manual, repeatable engineering processes and driving their automation
• Experience designing or scaling evaluation frameworks (offline and/or online) for machine learning models
• Demonstrated success reducing technical debt in a live, high-traffic production system without stalling feature delivery
• Deep technical fluency in machine learning systems: model training pipelines, feature engineering, model serving, and evaluation at production scale
• Experience managing Data Scientist and/or in-depth knowledge for data science
• Experience hiring, mentoring, and developing engineering talent
• 8+ years of overall hands-on engineering experience, including 4+ years managing software, data science or machine learning engineering teams
• Proven track record leading technical customer engagements or POVs, including direct interaction with enterprise customers
• B.S. or MS/Phd in Computer Science (or related technical discipline), or equivalent practical experience
• Familiarity with fraud detection, risk, or trust & safety domains
• Experience with large-scale distributed ML infrastructure such as Spark, Flink, Databricks, or similar
• Experience with streaming architectures (e.g., Kafka) and containerized/orchestrated deployments (Docker, Kubernetes)
• Hands-on experience with GCP or AWS ML infrastructure
• Familiarity with using AI co- Deadline to Apply: October 14, 2026 at 5:00 AM UTC
• 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