SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Peec AI is building the data plane for AI visibility, processing millions of chats, scrapes, citations, sources, and model responses through a high-performance backend. As a Senior Backend Engineer, you will own the systems powering this infrastructure—not just adding endpoints or closing tickets, but making scraping, chat-processing, source-ingestion, and analytics infrastructure more reliable, cheaper, faster, and easier for the team to reason about.
You will debug provider outages, design queueing models, ship TypeScript service changes, and write clear postmortems that guide the next engineer. The ideal candidate has built or operated systems with real constraints: rate limits, flaky third-party APIs, queues, webhooks, retries, distributed locks, data migrations, observability gaps, customer pressure, and cost ceilings.
Required experience: 5+ years in backend, platform, infrastructure, or distributed systems engineering in production. Deep hands-on TypeScript expertise is essential—you should be comfortable designing clean, reliable backend systems on Bun, not just writing application code. You need strong understanding of queues, workers, retries, rate limits, idempotency, distributed locks, webhooks, and failure handling.
You must have experience operating systems under real production pressure: incidents, provider outages, customer escalations, noisy alerts, slow dependencies, and incomplete data. Comfort with cloud-native infrastructure is required; the stack includes GCP, Kubernetes, Cloud Tasks, Pub/Sub, RabbitMQ, Redis, Postgres, Firestore, ClickHouse, Prometheus, Grafana, and OpenTelemetry.
You can debug across layers—application code, logs, metrics, queues, provider APIs, databases, network behavior, and deployment state. Product-minded judgment is important: you separate internal detail from customer-safe explanation and understand when infrastructure changes have customer, security, or cost implications. High ownership and low ego are essential; you take responsibility for ambiguous systems, ask sharp questions, and make the team better through clear thinking and kindness. You bias toward durable fixes, not quick restarts—you dig into retry amplification, backpressure issues, unsafe parser assumptions, or unmodeled provider limits.
The tech stack includes TypeScript and Python, Bun and Elysia runtimes, GCP and Kubernetes infrastructure, and observability tools like Prometheus, Grafana, and OpenTelemetry. You will work with LLM chats, AI search, source scraping, citation extraction, and integrations with OpenAI, Anthropic, Perplexity, Gemini, and other AI providers.
