Senior Machine Learning Engineer I - Ads Platform

Fetch is seeking a Machine Learning Software Engineer for its Ads Platform team. The role builds and improves machine-learning systems for ad ranking, relevance, and optimization, including feature pipelines, model workflows, and low-latency production serving. It partners with product, data, and platform teams to improve advertiser performance, user engagement, and marketplace efficiency.

Responsibilities:

• 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 to improve ranking quality
• Leverage LLMs to reduce model development and annotation effort, including synthetic data generation, assisted labeling, weak supervision, and error analysis for ranking and relevance tasks
• Own ML experimentation, offline and online evaluation, and production inference for assigned ad ranking components
• Partner closely with product, data, and platform teams to translate advertiser and user experience gaps into measurable ML improvements
• Maintain high standards for model performance, reliability, latency, and data quality in production ranking systems
• Use AI-assisted tools to accelerate development, experimentation, debugging, and analysis while maintaining strong engineering judgment
• Designing features and validating ideas with ChatGPT & Claude sandboxes
• Leveraging AI for code generation and technical prototyping
• Using AI assistants for systems architecture diagramming and design validation

Requirements:

• 5+ 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
• Strong engineering judgment and systems mindset, with an emphasis on reliability, performance, and long-term maintainability of ranking or optimization systems
• 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
• Experience with applied machine learning for relevance, ranking, or personalization problems (e.g., feature engineering, model evaluation, or feedback loops)
• Experience working in small, fast-moving, cross-functional teams, partnering closely with product, data, and platform stakeholders