Staff Software Engineer

What we’re building and why we’re building it. 

Every month, millions of people use Fetch earning rewards for buying brands they love, and a whole lot more. Whether shopping in the grocery aisle, grabbing a bite at the drive-through or playing a favorite mobile game, Fetch empowers consumers to live rewarded throughout their day. To date, we’ve delivered more than $1 billion in rewards and earned more than 5 million five-star reviews from happy users. 

It’s not just our users who believe in Fetch: with investments from SoftBank, Univision, and Hamilton Lane, and partnerships ranging from challenger brands to Fortune 500 companies, Fetch is reshaping how brands and consumers connect in the marketplace. When you work at Fetch, you play a vital role in a platform that drives brand loyalty and creates lifelong consumers with the power of Fetch points. User and partner success are at the heart of everything we do, and we extend that same commitment to our employees.

At Fetch, we value curiosity, adaptability, and the confidence to explore new tools, especially AI, to drive smarter, faster work. You don’t need to be an expert, but you should be ready to learn quickly and think critically. We welcome learners who move fast, challenge the status quo, and shape what’s next, with us.  Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, Fetch fosters a people-first culture rooted in trust, accountability, and innovation. We encourage our employees to challenge ideas, think bigger, and always bring the fun to Fetch.

Fetch is an equal employment opportunity employer.

Meet Fetch Engineering:

At Fetch, we are passionate about solving challenging problems and embracing ambiguity. Our engineering philosophy promotes adaptability and innovation over rigid adherence to rules. Our engineers thrive in complex environments, making well-informed decisions even in uncertain situations. We seek out the necessary information and focus on action and impact, while consistently upholding high technical standards. In this role, you will be a technical leader, shaping best practices to build world-class, user-facing technology. You will mentor fellow engineers, fostering technical growth and collaboration within the team. As a hands-on leader, you will actively contribute to the codebase and deliver features alongside your team.

About the Role

Fetch’s Core Services team is building the next generation of support experiences, powered by an LLM, grounded in trusted data, and designed with safety and accountability from day one. This work is user-facing and directly shapes how quickly and accurately customers get help, and how confidently agents can resolve issues.

We’re hiring a Staff Backend Engineer to design and evolve the systems behind an LLM-enabled support toolchain. You’ll build the backend architecture that allows a chatbot or LLM to make personalized, data-driven determinations about ticket type and recommended resolutions, while ensuring all actions, such as awarding points, are initially routed to human agents for review and approval. Recommendations will be evaluated for accuracy, monitored over time, and progressively unlocked so that in a later phase, ticket types that consistently meet accuracy and safety thresholds can be resolved end to end through automation with strong safeguards and auditability.

This is a high-impact role at the intersection of backend systems, data, and applied AI, where reliability, observability, and responsible automation are non-negotiable.

This is a full-time role that can be held from one of our US offices or remotely in the United States.

Responsibilities

• Design and scale core services for LLM-driven support tooling by building modular systems that classify customer issues, recommend ticket types, and propose resolution paths using real-time, trusted data.
• Build user-facing and agent-facing APIs for support decisioning by developing well-structured APIs that support consistent ticket intake, enrichment, routing, and recommendation outputs across automated chat surfaces and internal agent tools.
• Implement a secure customer context retrieval layer by building services that assemble only the necessary contextual data with strict access controls, PII minimization, and auditing, enabling safe, data-driven personalization.
• Build human-in-the-loop workflows for action approval by designing mechanisms that route LLM recommendations to agents for review with clear rationale, supporting evidence, and suggested responses, ensuring humans remain the final approvers in early phases.
• Develop evaluation, monitoring, and observability for LLM-powered support by instrumenting tracing, structured logs, and metrics to monitor end