Engineering Leader, ML Platform
About the Role
Attentive is building the next generation of ML-driven personalization and decisioning for messaging and marketing. We are looking for an ML Platform Engineering Manager to build and lead our Machine Learning Platform team. This team owns the foundational systems that enable our ML engineers and data scientists to train, deploy, and maintain production-grade ML models at massive scale.
You will define the platform roadmap, hire and grow the team, and deliver the tooling and infrastructure that makes ML work repeatable, observable, and easy to operate at scale. This role reports directly to the Head of Machine Learning.
What You’ll Accomplish
- Build and lead the ML Platform team (hiring, coaching, execution bar)
- Own the end-to-end ML platform roadmap and delivery: training + evaluation infrastructure, feature management, and standardized ML workflows
- Ship a clear “golden path” for ML development: CI/CD, champion/challenger rollouts, experimentation, model registry, and automated re-training
- Enable massively scalable deployments (batch and near-real-time), including rollout patterns (shadow/canary), robust contracts, and operational readiness (SLOs, runbooks, on-call)
- Lead ML observability and debugging across the stack (data quality, drift, performance, latency, cost), leveraging Ray + AnyScale
- Partner across ML Eng, Data Science, Analytics Eng, and Infrastructure to increase velocity and develop a world-class standard for Machine Learning integrations
- Drive cost and capacity efficiency for distributed compute (scheduling, resource governance, spend visibility)
Your Expertise
- 6+ years in software/data/ML-infra engineering, including 2+ years people management; experience building shared platforms adopted by multiple ML teams
- Strong distributed systems + cloud fundamentals; comfortable owning reliability (SLOs, incidents, on-call maturity)
- Production ML platform experience across the lifecycle: feature pipelines, training/eval, deployment, and monitoring
- Snowflake-native experience for modeling-ready data and feature management (data modeling, backfills, point-in-time correctness, governance/lineage) nice to have
- Hands-on experience with distributed computing (Ray, Spark, Dask) with Ray and/or AnyScale for distributed workloads and observability
- Familiar with orchestration (Metaflow, Airflow, Dagster, etc., data transformation pipelines (dbt), containerization (Docker/Kubernetes), and modern CI/CD practices
- Experience with ML observability and lifecycle management (MLflow)
- Clear technical leadership: roadmap setting, stakeholder alignment, and ability to get into the weeds when needed
You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.
For US based applicants:
- The US base salary range for this full-time position is $227,000 - $300,000 annually + equity + benefits
- Our salary ranges are determined by role, level and location
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