ML Data Platform Engineer: Scale AutoML & Feature Stores

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 ’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.

• 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

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, Click House, 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., Sage Maker, Vertex
  
  AI) or internal ML platforms