Backend Engineer, AI (Agent Systems)

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. 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. 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. 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.", "salary_raw": "Row(double=None, string=None)"}