Senior Machine Learning Engineer I

About the Role
We are seeking a Machine Learning Software Engineer to join Fetch’s Scan, Match & Catalog team. This role sits at the intersection of applied machine learning, data engineering, and production systems, with a focus on improving receipt understanding, product matching, and catalog enrichment at scale. You will partner closely with product, operations, and platform teams to deliver ML-driven automation, including computer vision and OCR pipelines, LLM-based workflows, and scalable ML services. This is a high-impact opportunity to help shape Fetch’s Scan-to-Catalog foundation and significantly increase automation, quality, and match coverage across the platform.
This is a full-time role that can be held from one of our US offices or remotely in the United States.
Role Responsibilities

• Build and scale ML models across the scan, match, and catalog pipeline, supporting receipt understanding, product matching, and catalog enrichment.
• Implement and iterate on active learning strategies, including data sampling, error-driven retraining, and human-in-the-loop workflows.
• Leverage LLMs to reduce model training and annotation effort, including synthetic data generation, assisted labeling, weak supervision, and error analysis.
• Own ML experimentation, evaluation, and production inference for assigned SMaC components.
• Collaborate with product, data, and platform partners to translate quality gaps into ML improvements.
• Use AI tools to accelerate development and improve system design, including:
  • Prototyping and validating ideas with LLM tools.
  • Leveraging AI for code iteration and experimentation.
  • Using AI assistants for architecture diagramming and design validation.
  • Exploring LLM-powered features where appropriate.

Minimum Requirements

• 4+ years experience in software engineering, with production-level coding experience.
• Strong proficiency in Python for ML development, with working knowledge of Go, and hands-on experience deploying models into production systems. Experience with AWS technologies and distributed systems.
• Practical experience applying LLMs to reduce training and annotation effort, including assisted labeling, synthetic data generation, weak supervision, or error analysis.
• Strong engineering mindset with the ability to deliver reliable, maintainable, and scalable systems.
• Experience with AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or similar) to improve development efficiency and code quality.
• Ability to critically evaluate AI-generated outputs, with strong debugging and problem-solving skills to validate correctness.

Preferred Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field. Equivalent practical experience considered in lieu of degree.
• Familiarity with AI tools and frameworks like AWS Bedrock, Langchain, vector databases, or similar AI orchestration technologies.
• Experience with machine learning workflows and large language models (LLMs).
• Familiarity with orchestrating ML-driven actions in high-complexity or high-throughput environments.
• Hands-on experience with computer vision and OCR, such as receipt/document parsing, layout-aware modeling, or image-based ML pipelines.