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Fetch is a rewards platform that has distributed over $1 billion in Fetch Points to users and processes more than 13 million receipts daily, providing visibility into $212 billion in gross merchandise value. The company operates an outcomes-based advertising platform helping brands acquire and retain consumers, backed by investors including SoftBank, ICONIQ, and DST.
You will join the Ads Platform team as a Senior Machine Learning Engineer, working at the intersection of applied ML, data engineering, and production systems. Your focus will be 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.
Key responsibilities include:
- Design, build, and improve machine learning models powering 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 through 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
- Partner with product, data, and platform teams to translate business gaps into measurable ML improvements
- Maintain high standards for model performance, reliability, latency, and data quality in production systems
- Use AI-assisted tools to accelerate development, experimentation, debugging, and analysis while maintaining strong engineering judgment
Fetch values curiosity, adaptability, and confidence in exploring new tools, especially AI. The company has been ranked as one of America's Best Startup Employers by Forbes for two consecutive years and fosters a people-first culture rooted in trust, accountability, and innovation.
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
- 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 (assisted labeling, synthetic data generation, weak supervision, model error analysis)
- Strong engineering judgment and systems mindset, with emphasis on reliability, performance, and long-term maintainability
- Experience using AI-assisted development tools (GitHub Copilot, ChatGPT, or similar) to accelerate iteration while maintaining code quality
- Ability to critically evaluate AI-generated outputs, debug complex issues, and validate correctness in production ML workflows
Preferred qualifications:
- Bachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience
- Familiarity with modern AI tooling and frameworks (AWS Bedrock, LangChain, vector databases, or similar orchestration technologies)
- Experience building and operating ML workflows involving large language models, including prompt-driven systems and model-assisted pipelines
- Familiarity with orchestrating ML-driven decisions in high-throughput or low-latency environments (ranking, recommendation, optimization)
- Applied ML experience for relevance, ranking, or personalization problems
- Experience working in small, fast-moving, cross-functional teams
