About this role
About ActAI
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Responsibilities
- Build and operate backend systems that serve AI-powered features in production.
- Design inference pipelines, orchestration layers, and service boundaries around models.
Requirements
- Own production concerns: monitoring, logging, alerting, and incident response.
- Optimize latency and throughput across inference, caching, batching, and streaming.
About Bjak
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations. Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things. Role As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. Your work sits between models and users, where latency, correctness, reliability, and cost directly impact real-world experience. You will build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients. Focus Build and operate backend systems that serve AI-powered features in production. Design inference pipelines, orchestration layers, and service boundaries around models. Own production concerns: monitoring, logging, alerting, and incident response. Optimize latency and throughput across inference, caching, batching, and streaming. Ide