Senior Machine Learning Engineer II - Ad Platform

We are seeking a Senior Machine Learning Engineer II to join Fetch’s Ad Ranking team. This role sits at the intersection of applied machine learning, data engineering, and production systems, with a focus on building and improving ranking, relevance, and optimization models that drive ad selection and delivery at scale. You will partner closely with product, data, and platform teams to develop ML-driven decisioning systems, including feature pipelines, model training and evaluation workflows, and low-latency serving infrastructure. This is a high-impact opportunity to influence how ads are ranked and personalized across the Fetch experience, improving advertiser performance, user engagement, and overall marketplace efficiency.

Design, build, and improve machine learning models that power ad ranking, relevance, and optimization across the Fetch platform.

Implement and iterate on active learning strategies, including data sampling, error-driven retraining, and human-in-the-loop workflows

Leverage LLMs to reduce model development and annotation effort, including synthetic data generation, assisted labeling, weak supervision, and error analysis

Own ML experimentation, offline and online evaluation, and production inference for assigned ad ranking components.

8+ years of software engineering experience with a strong track record of building and maintaining production ML or data-driven systems.

Strong proficiency in Python for machine learning and data processing, with working knowledge of Go, and hands-on experience deploying low-latency models into production ranking or decisioning systems.

Experience with AWS and distributed systems, including building or operating scalable training pipelines and online inference services.

Practical experience applying LLMs to reduce model development and data labeling effort, including assisted labeling, synthetic data generation, weak supervision, or model error analysis.

Experience using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, or similar) to accelerate iteration while maintaining high code quality.

Ability to critically evaluate AI-generated outputs, debug complex issues, and validate correctness in production ML workflows.

Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

Familiarity with modern AI tooling and frameworks such as AWS Bedrock, LangChain, vector databases, or similar orchestration technologies used in ML-powered decisioning systems.

Experience building and operating machine learning workflows involving large language models (LLMs), including prompt-driven systems and model-assisted pipelines.

Familiarity with orchestrating ML-driven decisions in high-throughput or low-latency environments, such as ranking, recommendation, or optimization systems.