Top Tech Jobs & Startup Jobs

Deploy and operate production generative AI and agentic workloads for enterprise customers. Design large-scale LLM and multimodal inference architectures, manage Kubernetes-based deployments, troubleshoot performance issues, integrate infrastructure into CI/CD workflows, and develop reusable Helm, Terraform, and automation solutions. Collaborate with customer engineering teams, contribute to platform reliability and observability strategies, and lead technical workshops on AI infrastructure best practices.

Own the full enterprise sales cycle for AI inference and serving solutions, from pipeline generation through closing strategic accounts. Build relationships with technical and executive stakeholders, manage technical proofs of concept, exceed revenue targets, and expand existing accounts. Represent the company at AI/ML events, support technical content and demonstrations, provide market and competitive insights, and bring customer feedback into product strategy.

Own the vision, strategy, and roadmap for FriendliAI’s AI inference platform, including model serving, deployment, orchestration, APIs, and developer capabilities. Partner with engineering, research, enterprise customers, and go-to-market teams to improve inference performance, scalability, reliability, usability, and cost. Lead product initiatives from discovery through launch, establish product planning and measurement practices, and mentor Product Managers and Designers.

Build and scale FriendliAI’s web platform for AI model deployment, monitoring, inference, agent workflows, authentication, organization management, billing, and usage metering. Develop robust APIs and data models, integrate infrastructure and orchestration systems, deliver features through the UI, and improve reliability through testing, CI/CD, code reviews, and architecture improvements. The role requires strong Python and TypeScript skills, backend experience, and familiarity with cloud-native systems.

Design, build, and operate a multi-cluster, multi-tenant Kubernetes fleet for GPU inference. Extend Kubernetes with controllers/CRDs, implement GPU scheduling and autoscaling, own the network data plane, design cross-cluster connectivity and service mesh, drive reliability/SLOs, deliver IaC with Terraform/Helm/GitOps, and collaborate with platform, SRE, and security teams.

Design, build, and operate a multi-cluster, multi-tenant Kubernetes fleet for GPU inference. Own cluster architecture, custom controllers and CRDs, GPU scheduling, autoscaling, Kubernetes networking (CNI, IPAM, DNS, load balancing), cross-cluster connectivity, service mesh, and reliability SLOs. Deliver infrastructure-as-code and collaborate with platform, SRE, and security teams to support latency-sensitive, high-traffic inference workloads.

Design, implement, and optimize high-performance GPU kernels (GEMM, attention, routing), develop CUDA/ROCm C++ code including low-level assembly, implement reduced-precision/quantized kernels (FP8/FP4), benchmark and ensure parity across NVIDIA and AMD, contribute to GPU libraries, accelerate multi-modal pipelines, and integrate next-generation GPU features into production inference engine.

Own and evolve core backend microservices for an AI inference platform: build production-grade APIs, multi-tenant SaaS features (auth, RBAC, billing), design OLTP/OLAP data models, collaborate on multi-cloud orchestration, ensure reliability/performance, and drive engineering quality through testing and CI/CD.

Design, build, and maintain agent APIs and production agent applications for document understanding, advanced RAG, and customer support automation. Integrate open-source models, collaborate with backend and infra for deployment and monitoring, and ensure APIs are robust, scalable, and developer-friendly.

Own and evolve core backend microservices for an AI inference platform, building production-grade APIs and multi-tenant SaaS capabilities (authentication, RBAC, billing). Design data models and pipelines across PostgreSQL and ClickHouse, collaborate on multi-cloud orchestration, ensure reliability and performance, and drive engineering quality through testing and CI/CD.