Machine Learning Engineer, Brand Ads

Team Overview Brands come to our platform for scale and for a content experience where their message lands in a way that feels native and relevant. Our team builds the delivery and inventory systems that make that possible for guaranteed delivery products, owning the path from inventory forecasting and allocation through pacing, traffic strategy, delivery quality, and brand safety. We partner closely with Product, Strategy, Data Science, and cross-functional engineering teams to turn open-ended business opportunities into capabilities that scale.

You will help build and evolve the systems behind a fast-growing portfolio of brand advertising products:

• Guaranteed delivery and pacing: the systems that meet committed delivery goals reliably while keeping delivery quality high.
• Inventory forecasting and allocation: the supply layer that predicts available inventory, allocates it across demand, improves utilization, and reduces waste.
• Traffic strategy and brand safety: the logic that arbitrates traffic between reservation and auction demand, and the controls that protect every impression.

Why Join Us You will work on a brand-facing business where your systems map directly to revenue and to the experience of major advertisers, solving real inventory and delivery problems at global scale and influencing both product and technical direction. If you are drawn to forecasting and allocation problems, pacing and delivery systems, and the kind of role where you own a product end to end, we would like to talk to you.

Responsibilities

• Build and optimize the delivery stack across pacing, allocation, traffic strategy, and delivery controls.
• Drive products from concept to launch with Product, Strategy, and cross-functional partners.
• Turn business goals into scalable delivery strategies and system designs.
• Own technical design, implementation, experimentation, launch, and iteration.
• Use data and experiments to diagnose problems and drive fast iteration. Minimum Qualifications
• BS/MS in Computer Science, a related field, or equivalent practical experience.
• Strong coding, debugging, and system design fundamentals.
• Proficiency in Go, C/C++, Java, Python, or similar languages.
• Comfort with ambiguity and the judgment to make clear engineering trade-offs.
• Strong cross-functional communication and collaboration skills.
• Experience with large-scale backend, recommendation, or advertising systems.

Preferred Qualifications

• Hands-on ads delivery experience in pacing, allocation, forecasting, traffic strategy, or guaranteed delivery, and familiarity with reservation or guaranteed product models.