Staff Machine Learning Engineer - Moloco Commerce Media

The Impact You’ll Be Contributing to Moloco

Design and deliver cutting-edge machine learning models that power Moloco’s Commerce Media products, serving as the core “engine” behind ads ranking, recommendations and search for retail media customers.

Advance the performance, scalability, and reliability of our core ad prediction and retrieval systems so customers can unlock meaningful business outcomes from their first campaigns onward.

The Opportunity

• Design, build, and productionize core ML models for CTR prediction, retrieval, conversion, and search/recommendation that directly drive performance for Moloco Commerce Media customers.
• Lead end-to-end experimentation cycles, from problem framing and offline modeling to online A/B testing and iteration, to continuously improve auction, ranking, and recommendation quality.
• Partner closely with product, engineering, and solutions teams to translate retail media and ecommerce requirements into robust ML solutions, features, and evaluation frameworks.
• Drive technical excellence across the ML stack by improving pipelines, model serving, and monitoring for low-latency, high-throughput, and highly reliable inference at scale.
• Mentor and uplevel other machine learning engineers through design reviews, knowledge sharing, and best practices in modern ML and MLOps.

How Do I Know if the Role is Right For Me?

• Bring 8-12 years of experience in applied machine learning or machine learning engineering, with a track record of shipping production models in ads, recommender systems, search, or large-scale ecommerce.
• Apply deep hands-on expertise with modern ML toolchains (e.g., Python, PyTorch/TensorFlow/JAX, feature stores, distributed training, and large-scale data processing frameworks) to solve complex ranking and prediction problems.
• Demonstrate experience building and optimizing models such as CTR predictors, retrieval models, conversion models, or recommendation systems, ideally including exposure to transformer-based architectures.
• Navigate ambiguity by breaking loosely defined or novel business problems into well-structured ML projects, defining metrics, setting up experiments, and iterating based on data.
• Collaborate effectively with cross-functional partners, tailoring complex technical concepts for non-technical stakeholders to drive alignment and decision-making across product, engineering, and go-to-market teams.
• Lead as the most senior technical IC on a team, independently owning high-impact technical initiatives, influencing architecture and roadmap decisions, and setting a high bar for code quality and model performance.
• Thrive in a fast-moving environment where combining rigorous experimentation with pragmatic engineering unlocks measurable value for advertisers, retailers, and end customers.