ML (Data) Platform Engineer

Position Overview Lyric is looking for an ML (Data) Platform Engineer to help scale our AutoML platform — a system deeply intertwined with data management, feature engineering, and time series forecasting at scale. You’ll play a foundational role in building scalable, composable data infrastructure and pipelines that support both ML and analytics use cases. This is not a vanilla ETL role — you’ll work on building data platforms that underpin automated model building, experimentation, and data lineage systems with high SLA requirements. If you thrive at the intersection of ML, data infrastructure, and platform thinking, this is a high-impact opportunity. Key Responsibilities Build and scale data management systems that power our AutoML and forecasting platforms Own and evolve the feature store and feature engineering workflows Implement robust data SLAs and lineage systems across time series data pipelines Collaborate with ML engineers, infra, and product teams to ensure scalable and user-aware platform design Drive architectural decisions around data distribution, versioning, and composability Participate in the design of reusable systems for varied supply chain problems Qualifications Must-Have: Strong experience working with large-scale data systems (Big Data, distributed pipelines) Hands‑on experience with ETL pipelines, data lineage, and data reliability tooling Proven experience in ML feature engineering and/or building feature stores Exposure to time series data, forecasting pipelines, or AutoML workflows Strong problem‑solving and design thinking ability — can break down ambiguous platform problems Good-to-Have: Familiarity with modern data infra (e.g., Apache Iceberg, ClickHouse, Data Lakes) Product thinking — can anticipate how users will interact with the system and build accordingly Experience building composable, user‑extensible systems Prior exposure to AutoML frameworks (e.g., SageMaker, VertexAI) or internal ML platforms #J-18808-Ljbffr Lyric