About the role
About Sesame Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive.
About
the Role Backend engineering at Sesame is a domain rich with challenging technical problems: real-time streaming audio pipelines, resilient and low-latency networking, complex machine learning inference, scalable agentic workflows, and more. Our work ranges from systems programming to thorny distributed systems design to cutting-edge applied AI. At the centre of all of this: how should we think about quality, craft, security, and reliability engineering when the system under test is a human-like personality? On the backend engineering team, your job is to tackle challenges like these directly, all while providing a reliable, secure, high quality consumer experience for a growing base of users.
Responsibilities Design and build the backend systems and services that power Sesame's product. You own the data models, the APIs, and the distributed systems that everything else depends on Write software that's built to last. You're not prototyping; you're solving hard problems that require careful thinking about scalability, reliability, and correctness Build and evolve the frameworks and libraries that other engineers build on. You care about good software design and it shows in the interfaces you create Own the full lifecycle of your services: schema design, implementation, deployment, performance tuning, and on-call Work across the data layer, choosing and operating the right stores for the job: relational databases, NoSQL, queues, caches, search indexes Identify performance bottlenecks and fix them. Think about cost, throughput, and latency as first-class concerns Own the architecture of systems where ML models are a critical component but not the whole story. Real-time audio pipelines, agentic orchestration, stateful conversation systems — these are complex, ML-driven machines that require careful architecture Spot opportunities to improve developer efficiency within your area. You might prototype a tool or workflow improvement, then hand it off to the infra team to productionize Required