Founding ML Engineer

About Us

We’re Apptor.AI. We’re building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.

If you want to build category-defining infrastructure with a team that ships weekly, let’s talk.

About the Role

You’ll build the platform our data science team works on.

That means the pipelines, tooling, and infrastructure that let data scientists train, deploy, and monitor models without needing an engineer at every step - and working alongside them to get their models onto the platform when they do.

The work is predictive ML: churn, purchase timing, and recommendations, running for enterprise clients at scale.

You’ll help shape how ML is built and operated here as the team grows.

Responsibilities

• Build the ML Platform - Design the pipelines, tooling, and infrastructure the data science team builds on.
• Enable Self-Service - Make it possible for data scientists to train, deploy, and monitor models without an engineer in the loop.
• Implement Models on the Platform - Work alongside data scientists to take their models from notebook to production.
• Own Features at Scale - Build the feature pipelines that keep training and inference consistent.
• Keep Models Healthy - Build the monitoring and retraining that keep predictions accurate over time.
• Shape How We Build ML - Help define the tooling, standards, and practices for ML as the team grows.

Requirements

• 3+ years of production ML engineering.
• Experience building ML platforms or the tooling data scientists work on.
• Experience with Airflow or similar orchestration tools.
• Experience building and running ML pipelines in production.
• Experience monitoring models in production: drift, decay, and knowing when to retrain.
• Strong Python and SQL.

Bonus

• Familiarity with SageMaker.
• Experience with experiment tracking and model registries.
• Experience deploying models per client in a multi-tenant setup.