Top Tech Jobs & Startup Jobs

Architect and build security foundations for a multi-tenant, Kubernetes-based serverless platform. Responsibilities include implementing Terraform and Helm guardrails, network segmentation, service-mesh policies, secrets and key management, container runtime detections, IAM and workload identity, threat modeling, production issue resolution, post-incident hardening, and compliance evidence automation.

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.

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.

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.

Design and optimize high-performance GPU kernels (GEMM, attention, routing) for AI inference across NVIDIA and AMD GPUs. Implement CUDA/C++ and low-level assembly code, build reduced-precision/quantized (FP8/FP4) kernels, benchmark cross-vendor performance, contribute to internal GPU libraries, accelerate multi-modal pipelines, and integrate next-generation GPU features into production.

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.

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.