Fetch is seeking a Machine Learning Software Engineer to join their Fraud team to automate workflows that protect a $300M+ revenue stream by developing scalable ML-driven solutions, including microservices, LLM-based workflows, and data pipelines.
Requirements
- 2+ years experience in software engineering, with production-level coding experience.
- Proficiency in Java or Go, with a strong background in microservices and coupled architectures.
- Experience with AWS technologies and distributed systems.
- Working knowledge of Flink or equivalent data/stream processing frameworks.
- Solid understanding of event-driven and async architectures, including long-running processes.
- 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.
Responsibilities
- Develop scalable backend services and microservices in Java or Go to support ML-driven orchestration.
- Build and optimize data pipelines and infrastructure to support event-driven, async, and long-running ML processes.
- Partner with engineering teams to automate workflows, integrate models, and ensure revenue protection.
- Educate internal stakeholders on ML-driven decision-making and create transparent, traceable systems for fraud management.
- Drive automation and orchestration of workflows across fraud, billing, and manual operations teams.
- Leverage AI-assisted development tools (e.g., GitHub Copilot, ChatGPT) to accelerate prototyping, code generation, debugging, and documentation.
- Evaluate and integrate AI-powered solutions into workflows to improve productivity, model experimentation, and system efficiency.
Other
- Full-time role that can be held from one of our US offices or remotely in the United States.
- Strong collaboration and communication skills, with the ability to explain technical concepts to diverse stakeholders.
- Ability to critically evaluate AI-generated outputs, with strong debugging and problem-solving skills to validate correctness.
- Previous experience working in small, fast-moving, cross-functional teams.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Equivalent practical experience considered in lieu of degree.
