Top Tech Jobs & Startup Jobs

Act as technical liaison for customers using FriendliAI's inference platform: onboard developers, provide technical support, create documentation and tutorials, debug production issues with engineering, and run demos and Q&A sessions.

Design, build, and maintain a scalable web platform and APIs for deploying and monitoring multimodal AI models and agent workflows. Collaborate with product, infrastructure, and design teams to optimize performance, ensure reliability, drive CI/CD and testing, and contribute to long-term architecture decisions for a cloud-native, multi-tenant SaaS system.

Lead strategy and roadmap for FriendliAI's inference platform, owning initiatives end-to-end. Mentor junior PMs/designers, drive customer discovery, define product requirements and KPIs, partner with engineering/research, and align with GTM and sales to deliver scalable model APIs, deployment workflows, and developer features.

Lead end-to-end enterprise sales for FriendliAI's AI inference platform: generate pipeline, close high-value deals, run technical POCs, engage AI/ML communities, collaborate with engineering, and inform product roadmap.

Design, implement, and optimize GPU kernels, kernel compiler, memory planner, and runtime for low-latency generative AI inference. Analyze performance bottlenecks across hardware and software, collaborate with infrastructure teams, and maintain production profiling, benchmarking, and validation tooling while supporting new model architectures and multi-GPU strategies.

Design, deploy, and operate large-scale LLM and multimodal inference architectures. Work hands-on with customer engineering teams to containerize, scale, monitor, and troubleshoot GPU-based inference workloads across Kubernetes, CI/CD, and hybrid/on-prem environments. Create Helm charts, Terraform modules, and observability tooling while delivering workshops and platform reliability insights.

Design, build, and maintain agent APIs and production agent applications (document understanding, RAG, automation). Integrate open-source LLMs and multimodal models, collaborate with backend and infra teams for deployment, and ensure APIs are reliable, scalable, and developer-friendly with strong documentation and monitoring.

Build and optimize GPU kernels and core inference engine components (compiler, memory planner, runtime) for latency-critical generative AI workloads. Profile and benchmark performance, collaborate with cloud/infrastructure teams, support new model architectures and multi‑GPU/distributed inference, and maintain production-grade validation tools.