A1
Backend Engineer, AI Systems
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About A1
There are over 5 billion users using basic applications today such as email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organizing, and workflows, with minimal prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyably with over ~90%* reduced time.
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 the 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.
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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.
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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 the hands of our users a truly magical product.
Interview process
If there appears to be a fit, we'll reach out 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.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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