Founding Engineer
Founding Engineer builds knowledge engines, multimodal agents, voice AI, and codegen systems for complex physical product support. Owns end-to-end customer deployments in fast-paced startup environment.
What You'll Work On
Knowledge Engine
- Build the core engine that stores, relates, and reasons over product knowledge: manuals, compatibility matrices, installation guides, field observations, tribal knowledge.
- Deep infrastructure that the rest of the company builds on top of.
Emotionally Intelligent Voice AI
- Build phone support where a customer calls in, describes their problem, and gets walked through a solution by an AI that understands tone, frustration, urgency, expression.
- SIP trunking, telephony infrastructure, audio processing.
Code Generation as a Primitive
- When something is too cognitively hard to explain in words, generate real-time diagrams, interactive schematics, visual walkthroughs through code.
- Leverage code generation tools and understanding of how people process information.
Owning Customers
- Own customer contracts end-to-end: demo to deployment to onboarding to ongoing.
- Talk to public companies, huge private companies, and lead pilots yourself.
Senior Machine Learning Operations Engineer
Build and operate Mercury's real-time ML inference platform for fraud risk decisioning. Own model deployment, observability, and lifecycle tooling with strong backend Python fundamentals.
Machine Learning Engineer - Embedded Insights
Drive ML initiatives from concept to production on the Embedded Insights team. Identify opportunities, build and deploy models using Plaid's financial datasets, and partner with product teams to deliver scalable customer-facing intelligence products.
Machine Learning Engineer
Advance Plaid’s foundation models by developing novel architectures, pretraining objectives, and fine-tuning strategies. Work across the full ML stack from data engineering to production serving and monitoring.