Founding Platform Engineer
About the role
Founding Platform Engineer role at Hyperspell, a YC-backed company building the memory and context layer for AI agents. High-autonomy, end-to-end ownership of infrastructure to scale, monitor, and optimize production systems in San Francisco.
Responsibilities
- Scaling to the next order of magnitude: design and evolve systems to absorb 10x traffic increases
- Bulletproof data pipelines: concurrency control, deduplication, fault tolerance to ensure correct processing of millions of documents
- Making it fast: profile, benchmark, and optimize to reduce latency and improve API performance
- Observability that tells a story: build dashboards, traces, and alerts for production visibility
- Infrastructure as a product: treat uptime, performance, and developer experience as features
- Within 30 days, map bottlenecks and migrate legacy services to scalable alternatives
- Within 60 days, implement continuous monitoring across environments and use agentic AI to resolve problems proactively
- Within 90 days, build a scalable distributed ETL pipeline for ingestion and memory extraction
Qualifications
- 3-5 years of experience in backend, platform, or infrastructure engineering
- Strong fluency in Python
- Deep understanding of databases, networking, and distributed systems
- Experience operating production services on cloud infrastructure
- Clarity of thought, excellent communication
- Ability to work on location in San Francisco
Skills
Benefits
- Health, dental, and vision coverage and generous health benefits
- $10,000 to build your dream work setup
About Hyperspell
Memory for AI Agents.
Hyperspell (YC F25) gives AI agents memory. It connects to tools like Slack, Gmail, Notion, and Drive so agents can recall, reason, and remember across company knowledge. Developers use Hyperspell’s API to turn disconnected agents into AI coworkers that understand their work. Conor and Manu founded Hyperspell after building their own workplace agent and seeing how limited AI is without memory. They have over 15 years of experience in machine learning and have scaled API-based products to $30M ARR.
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