Full-Time | Remote / Hybrid | Engineering

About the Role

We're building the intelligence layer behind Superscale — and we need someone who can turn raw data into an unfair advantage.

As our Senior/Staff Data & ML Engineer, you'll own the entire data stack: from building the warehouse that powers product and business decisions, to developing ML models that make our AI-generated ads outperform anything on the market. You'll work with a large proprietary dataset - the kind of moat most startups can only dream of.

This is a foundational hire. You'll shape how we collect, structure, and leverage data across the company — from product analytics and funnel insights to predictive models that help our customers win. If you're the kind of engineer who gets excited about building a data platform from near-zero and then using it to ship ML features that move revenue, this is your role.

We believe in hiring for breadth and building leverage through AI tooling. You'll be a full-spectrum engineer who uses coding agents and modern tooling to operate at 10x the output of a traditional team.

Key Responsibilities

• Design and build our data warehouse from the ground up on top of cloud-native infrastructure, creating the single source of truth for product, marketing, and customer data
• Architect data pipelines that capture the full picture: user funnels, product usage, AI agent performance, and campaign outcomes
• Develop ML models that leverage our proprietary ad creative dataset to generate higher-performing assets — turning data volume into product quality
• Build predictive systems that forecast ad campaign performance, not just individual asset metrics — helping customers allocate budget before they spend it
• Integrate and analyze ad platform data from connected Meta and TikTok accounts to surface cross-platform insights that no single-platform tool can provide
• Create robust data models and APIs that make insights accessible to the product team, AI agents, and end users
• Establish data quality frameworks, monitoring, and observability so the team trusts the numbers
• Collaborate closely with product and engineering to embed data and ML capabilities directly into the product experience
• Evaluate and adopt modern data tooling (dbt, Airflow, Dagster, etc.) — picking what's right for our scale and trajectory, not what's trendy

Requirements

• 5+ years of experience in data engineering, with hands-on ML/data science work — you've built pipelines and trained models in production
• Strong foundation in SQL, Python, and modern data stack tooling (warehouses, orchestration, transformation)
• Experience designing data warehouses or lakehouses from scratch or near-scratch — you know how to make architectural decisions that scale