Key skills
Machine Learning Platform EngineeringDistributed SystemsCloud FundamentalsProduction ML LifecycleFeature PipelinesModel TrainingEvaluationModel DeploymentML MonitoringSnowflake Data ModelingDistributed ComputingRaySparkDaskOrchestration ToolsMetaflowAirflowDagsterData Transformation PipelinesdbtContainerizationDockerKubernetesCI/CD PracticesML ObservabilityMLflowSLOs ManagementIncident ManagementOn-call OperationsStakeholder AlignmentAIMachine LearningMLAnalyticsSnowflakeCI/CDLeadership
About this role
Attentive is an AI marketing platform focused on redefining brand and customer connections through personalized messaging. The Engineering Leader for the ML Platform will build and lead a team responsible for developing foundational systems that facilitate the training, deployment, and maintenance of production-grade ML models at scale.
Responsibilities:
- 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)
Requirements:
- 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
- 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
- Snowflake-native experience for modeling-ready data and feature management (data modeling, backfills, point-in-time correctness, governance/lineage)