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.

Senior Machine Learning Engineer I

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