Join a small, high-talent founding team to architect and build core backend systems for an AI-powered desktop assistant. You will own data infrastructure and AI runtime capabilities, driving scalability, low-latency performance, and innovative features for a category-defining consumer product. This role demands exceptional backend engineering with a spike in AI/agentic systems or large-scale data infrastructure.
Key Responsibilities:
- Architect and build core backend systems for an AI-powered desktop assistant, focusing on scalability and low-latency performance
- Own and evolve data infrastructure, tackling challenges in data storage, compression, and high-speed querying at scale
- Develop and optimize AI runtime and agent orchestration, building capabilities for context management, tool use, and retrieval
- Design and implement robust, scalable cloud infrastructure using GCP and Terraform to support a growing user base
- Ship consumer-facing features and platform capabilities, iterating quickly based on user feedback with the founding team
Backend engineering expertise in Python with experience in cloud infrastructure like GCP
Experience in data storage, compression, and pipeline optimization
Background in AI engineering and distributed systems
0-5 years of experience in backend engineering, focusing on either AI systems or large-scale infrastructure
Experience with large-scale cloud infrastructure such as GCP and Terraform
High-intensity, proactive mindset with extreme ownership
Ability to work full-time in-office in the SF Bay Area
Preferred:
Experience at VC-backed AI startups, FAANG/large tech, or top-tier quant trading firms
Background in competitive programming or graduation from top academic institutions
Built projects from scratch using LLMs, RAG, or embeddings
Provides a portfolio like GitHub demonstrating technical depth
Strong technical skills from competitive environments