Founding Machine Learning Engineer

Responsibilities

• Define Adaptive's ML strategy: where ML should be applied across our products, what infrastructure we need, and how we should approach build vs. buy decisions.
• Design and build production ML systems end-to-end — data pipelines, model training, evaluation frameworks, and inference serving.
• Establish evaluation methodology. Define how we measure model quality, catch regressions, and make data-driven decisions about model changes.
• Own the strategy for getting the data you need, in the format you need it — what/how to label, how to build feedback loops, and how our models improve over time.
• Partner with product engineers to integrate ML into the product. You will write production code and work within our existing codebase.
• Build and lead the ML team as scope grows.

Qualifications

• 8+ years of experience building ML systems in production, ideally with experience standing up the ML function at an early stage startup or as the senior or lead ML person at a previous company.
• Strong software engineering fundamentals. You write production-quality code in modern languages (Python, Java, TypeScript) and work within large codebases.
• Experience with cloud ML infrastructure (AWS SageMaker, Bedrock, Modal, Baseten, or similar).
• Experience with common ML and data processing frameworks (PyTorch, Tensorflow, Spark).
• Comfortable working across the stack — infrastructure, backend services, and data systems.
• Track record of mentoring MLEs and other engineers with observable, clear improvements in those you've worked with.
• High autonomy. You'll have support and context from leadership, but you're expected to define the path forward and drive it.