Senior Machine Learning Engineer, Causal & Decision Systems

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:

We care more about exceptional technical ability and judgment than matching a checklist. Strong candidates will have experience in several of:

Machine learning and statistical modeling

Causal inference and experimentation

Recommendation, advertising, pricing, marketplace, credit, or other decision systems

Bandits, reinforcement learning, optimization, or active learning

Uncertainty estimation

Counterfactual evaluation

Production ML systems

Python, SQL, and large behavioral datasets

Most ML systems learn from a dataset. Here, the decisions made by the model influence the data the model sees next. That creates a continuous loop: decision, intervention, outcome, learning, better decision.

The long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.

Real-world impact. The systems you build will run live commercial decisions across a portfolio of consumer brands, so you will see measurable economic outcomes from your work, not just offline benchmark improvements.

Technical growth at the frontier. Causal decision systems that learn from their own interventions are still an open problem. You will work at the intersection of causal ML, bandit algorithms, and production engineering, with the latitude to choose the right method for the problem.

Full ownership. You will own problems end to end, from framing and modeling through production deployment and evaluation.

Competitive benefits. We offer an attractive benefits package including primarily remote work, private medical and life insurance, additional paid time off, monthly allowances and reimbursements, employee discounts, and opportunities for professional growth.

Recruiter Screen: A conversation with our recruiting team to cover your background, the role, and mutual fit.

Virtual Interview Rounds: Focused discussion with the hiring manager & deeper conversations with cross-functional engineering and data science collaborators covering technical depth, system design, and working style.

In-Person Interview: A final on-site visit at our Costa Rica office to meet the broader team and connect with key stakeholders.

Reference Checks: Conducted in parallel with the final stages where possible.

Offer: We move quickly for the right candidate.

Interview process is subject to change. Any updates will be shared promptly and clearly.

Part of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately.

Due to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered . If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.

CSC Generation is an equal opportunity employer. All qualified applicants will receive consideration without regard to any characteristic protected by law. If you need an accommodation during the application process, contact [email protected] .

Employment is contingent on a background check, including verification of prior employment dates.