ML (Data) Platform Engineer

About the Company

Why We Built Lyric: Supply chains are more critical and complex than ever. Every day, large enterprises navigate trillions of possible decisions that could impact the bottom line. Powerful algorithms and AI can address these problems, yet most organizations struggle to leverage supply chain AI at scale. The current SCM technologies are either rigid, limited-scope point solutions or custom solutions built in-house, which demand immense expertise and investment.

That is…until now.

Enter Lyric: Lyric is an enterprise AI platform built specifically for supply chains, offering the best of both worlds:

• Out-of-the-box AI solutions for optimizing networks, allocating inventory, scheduling routes, planning fulfillment capacity, promising orders, propagating demand, building predictions, analyzing scenarios, and more, plus

• A platform-first approach that empowers both business and technical users with end-to-end product composability, leveraging no-code tools, their own code, or even forking our code to build and refine supply chain decision intelligence

With Lyric, enterprises no longer have to choose between flexibility and speed—they get both.

The Mission: We’re building a new era in supply chain with the team best equipped to lead it. With over 20 years at the intersection of supply chain and algorithms, we developed a deep conviction that global supply chains needed something like Lyric. Since our inception in December 2021, that conviction has been validated time and time again.

Today, a growing number of Fortune 500 companies, including Smurfit WestRock, Estée Lauder, Coca-Cola, Nike, and more, are innovating on their own terms with Lyric. We can’t wait to see what our customers, both current and future, are empowered to build with us next. Come build with us!

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