Location: San Francisco
Stage: Pre-Seed/Seed
About Amdahl
We're building the data infrastructure and agent orchestration layer for AI-native B2B marketing teams. Tools like ChatGPT and Claude generate text, but they don't know your customers, your past work, or what your teammate tried yesterday – so every prompt starts from scratch and everyone gets different answers.
Amdahl's ML-based data pipeline unifies a business's data across their go-to-market technology stack (call notetakers, support tickets, CRM), where our agent orchestration layer runs workflows to produce high-performing campaigns. Amdahl gives 10–100 person teams a shared, durable source of truth that turns raw data into insights, messaging, and content.
About the Role
We're looking for a founding member to join our technical team. You will own the applied machine learning discipline, from data pipeline to architecting and implementing production ML systems. We're a small team where everyone wears multiple hats.
What You'll Do
- Founding data infrastructure
- Architect and maintain data pipelines that power our ML solutions and agent workflows as we scale from 0 to 1
- Evolve our unified data schema across customer integrations
- Build internal tools and processes to ensure data quality, reliability, and accessibility for the team as needed
- Develop production ML systems
- Experience with semantic analysis, sentiment detection, or user behavioral analytics
- Understanding of what signal you can realistically extract from text vs. audio vs. video - and the cost / benefit tradeoffs
- Develop solutions that better augment our data set and optimize for natural language search / semantic search
- Build solutions that work under real-world constraints (latency, cost, reliability)
- Bonus: experience with behavioral prediction, or similar sensitive domains
- Wear multiple hats as a founding member of the technical team
- Engage directly with customers to understand their problems
- Contribute to product thinking, not just technical execution
- Be comfortable context-switching between deep technical work and external-facing conversations
Who You Are
Technical depth
- 5+ years building ML systems in production environments
- Strong fundamentals in optimization, control theory, or applied mathematics – you can formulate problems on paper