Full Stack/Backend EngineerSaasventurecapital • San Francisco, California, United States
Full Stack/Backend Engineer
Saasventurecapital
- San Francisco, California, United States
- San Francisco, California, United States
Über
Full-Time · San Francisco, CA · In-person preferred, hybrid/remote possible
~ $100k-200k/yr
Apply:
https://airtable.com/app16PZxY2eVBKHL0/pagLyUbKm36xxcn8D/form
About the Role Daemo AI is an intelligence platform catering to middle-market and enterprise clients. This engineer owns the full stack — from real-time data pipelines and LLM summarization engines to the user-facing web portal.
Required Skills Languages
TypeScript (Expert)
— all production frontend/backend code
Python (Proficient)
— pipelines, scraping, LLM orchestration
SQL / MongoDB (Proficient)
— BigQuery ingestion, aggregation pipelines
Bash/Shell
— EC2 ops, cron, deployment scripts
Rust
— systems on Rust
Frameworks & Infrastructure
Next.js (App Router), React, Tailwind CSS / shadcn/ui, NextAuth.js
Node.js + Server Actions, OpenAI-compatible API (streaming, tool calling)
MongoDB Atlas — document modeling, Atlas Search
Docker / Docker Compose — all services containerized
Linux / tmux — SSH, process management, EC2 service administration
Engineering Capabilities
Full stack ownership: data model → API → UI → deploy (no tier handoff)
Async/concurrent systems & data pipeline architecture (ETL/ELT, deduplication)
Web scraping at scale — anti-bot evasion, government/political sources (Playwright, BeautifulSoup)
Real-time streaming — SSE/WebSockets for agent responses
Production debugging across multi-stage pipelines
Multi-tenant security — data isolation, access control, audit logging
AI / LLM
LLM API integration — chat completions, tool/function calling, streaming, context management
Prompt engineering for summarization, personas, research agents
Agent architecture — multi-step tool use, memory patterns, RAG
AI output evaluation — hallucination detection, guardrails
Preferred Skills
Rust
— core system runs on Rust
AWS Bedrock
— Claude 3.x families; cost/quality/latency tradeoffs
AWS (EC2, S3), Cloudflare
(DNS, tunnels), CI/CD (GitHub Actions)
Google BigQuery
— large-scale parallel data ingestion
Redis
— caching, job queuing, pub/sub
Signal CLI — secure messaging integration on Linux EC2
Multi-provider LLM routing (Fireworks AI or similar)
Embedding + vector search (Voyage AI or equivalent)
Automated briefing pipelines — scheduled ingest → summarize → deliver
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Sprachkenntnisse
- English
Hinweis für Nutzer
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