Senior Machine Learning Engineer, Causal & Decision Systems

You will help build systems that:

**estimate causal response + quantify uncertainty → choose actions → generate useful information → observe outcomes → update policies → evaluate challengers → deploy within guardrails**

We want to answer questions such as:

- What happens **because we change a price**, rather than simply what happens next? - How should uncertainty affect a decision? - When should the system exploit what it knows versus experiment to learn? - Can we estimate the value of a challenger policy before fully deploying it? - How do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints?

Depending on your background, you may work across:

- causal and heterogeneous treatment-effect modeling; - uncertainty estimation and calibration; - contextual bandits, active learning, or sequential decision-making; - policy learning and constrained optimization; - counterfactual and off-policy evaluation; - experimentation and champion/challenger systems; - production ML infrastructure, monitoring, and automated deployment.

We care about selecting the right method, not using a particular framework.

Success is not a better offline metric.

The systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions.

Over time, the goal is simple:

**the system should become better at operating the business because it has operated the business.**