Backend Engineer

Summary

AlphaSignal is building the intelligence layer for AI, creating a real-time platform that ranks important updates in the field. The Backend Engineer will be responsible for managing the infrastructure and pipelines that support the AI platform and newsletter.

Responsibilities

• You'll own the pipeline behind the platform: scrapers, an LLM enrichment and editorial layer, a MongoDB data layer, a ranking system that decides what matters, and the foundations of our knowledge graph
• When throughput drops or something breaks, you diagnose it, ship the fix, and verify the recovery

Skills

• 3+ years of backend engineering with production ownership of a non-trivial system
• Python at a professional level — backend services, ML models, algorithms, and production-grade scripting
• Deep LLM fluency across providers (Claude, GPT, Gemini) — shipped production features, fluent with prompting, structured output, cost optimization, and failure modes
• Database depth — MongoDB, Postgres at scale, or comparable; indexes, query plans, atomicity, replication
• ML / vector search — recommendation systems, knowledge graphs, entity linking, and the full vector DB landscape (Pinecone, Weaviate, Qdrant, Milvus, pgvector, Chroma, FAISS, plus Atlas/Elasticsearch/OpenSearch)
• Designed systems for large, growing traffic — caching, batching, async, and index tuning
• AWS / infrastructure — comfortable deploying and operating services on EC2 and the broader Amazon suite
• Fluent with modern AI dev tooling (Cursor and similar)
• Solid engineering practices: observability, monitoring, and keeping a codebase healthy as it scales
• Experience at a high-traffic content platform (Medium, Substack, Reddit, etc.)
• Familiarity with arXiv, GitHub, Hugging Face, or X APIs
• Content systems: feeds, aggregators, recommenders
• Scraping at scale, deeper prompt engineering, or early-stage startup experience

Qualifications

Must Haves

• 3+ years of backend engineering with production ownership of a non-trivial system
• Python at a professional level — backend services, ML models, algorithms, and production-grade scripting
• Deep LLM fluency across providers (Claude, GPT, Gemini) — shipped production features, fluent with prompting, structured output, cost optimization, and failure modes
• Database depth — MongoDB, Postgres at scale, or comparable; indexes, query plans, atomicity, replication
• ML / vector search — recommendation systems, knowledge graphs, entity linking, and the full vector DB landscape (Pinecone, Weaviate, Qdrant, Milvus, pgvector, Chroma, FAISS, plus Atlas/Elasticsearch/OpenSearch)
• Designed systems for large, growing traffic — caching, batching, async, and index tuning
• AWS / infrastructure — comfortable deploying and operating services on EC2 and the broader Amazon suite
• Fluent with modern AI dev tooling (Cursor and similar)
• Solid engineering practices: observability, monitoring, and keeping a codebase healthy as it scales

Nice to Haves

• Experience at a high-traffic content platform (Medium, Substack, Reddit, etc.)
• Familiarity with arXiv, GitHub, Hugging Face, or X APIs
• Content systems: feeds, aggregators, recommenders
• Scraping at scale, deeper prompt engineering, or early-stage startup experience

Benefits

• Full ownership of a critical, high-impact system.
• Direct work with the founder.
• Remote, async-first, low-meeting culture.
• Four weeks PTO 
• Healthcare, dental and vision covered 80%