Backend Engineer, AI Systems

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 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. 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 and orchestration layers that handle multi-step workflows, tool calls, and retries Manage the full lifecycle of AI requests: routing, caching, batching, streaming, and state management Optimize latency, throughput, and cost across model inference and downstream systems Design systems that remain reliable despite non-deterministic model behavior and external dependencies Implement observability for AI systems, including logging, tracing, and debugging of model outputs and failures Collaborate with ML and product teams to translate model capabilities into stable, production-grade APIs

Ideal Experiences Strong backend engineering fundamentals in production environments. Experience running high-throughput, low-latency services. Familiarity with AI inference patterns (LLMs, embeddings, multimodal). Comfortable debugging distributed systems under load. Bias toward shipping and learning from production behavior.

Outcomes Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput. Multi-step AI workflows complete successfully across tools and services, with robust handling of failures and retries APIs are stable, clear, and support seamless integration with frontend and ML systems. Production incidents are quickly detected, diagnosed, and resolved, minimizing user impact. Iterative improvements based on real usage continuously increase system performance and reliability. System design evolves to support increasing scale, complexity, and new AI capabilities without major rewrites.

Tech Stack Python NodeJs Pytorch OpenAI / Anthropic / open-source LLMs SQl & noSQL Kubernetes Docker

How We Work The best products today in the world were built by small, world class teams.

We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning.

Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product

Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.