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Lead design and delivery of high-scale, resilient distributed inference data-plane services for LLM hosting. Optimize performance (tensor/data parallelism, KV-cache routing, prefill/decode disaggregation), collaborate cross-functionally, contribute upstream to open-source inference projects, mentor engineers, and operate production services with strong SLOs and observability.

Lead benchmarking and performance optimization for AI inference at GPU/kernel levels. Solve memory bandwidth and compute bottlenecks, implement quantization and parallelization (multi-node), advise on hardware/software stacks, mentor engineers, and collaborate to ship high-performance inference features.

Lead design and implementation of high-scale, multi-tenant AI inference data plane services. Optimize distributed LLM serving (prefill/decode disaggregation, KV-cache routing, autoscaling, flow control), contribute upstream to open-source inference projects, mentor engineers, and operate resilient, observable production services running on Kubernetes and specialized AI hardware.

Lead design and implementation of high-scale, resilient AI inference data plane services. Architect distributed LLM serving systems, optimize performance (tensor/data parallelism, KV-cache routing), build on Kubernetes-native inference frameworks, contribute to open-source projects, mentor engineers, and operate production services with strong observability and SLOs.

Lead design, build, and operate high-scale, resilient inference data plane services for LLMs. Drive distributed system architecture, performance optimization (tensor/data parallelism, KV-cache routing), Kubernetes-native serving frameworks, open-source contributions, mentorship, and production SLO-driven operations to deliver Inference-as-a-Service with low latency and high availability.

Lead architecture and low-level optimizations for high-performance AI inference. Benchmark and tune GPU kernels, memory and precision management, parallelize across multi-node GPU clusters, implement quantization (FP8/INT8/FP4), advise on hardware/software stacks (CUDA/ROCm/Triton/TensorRT), mentor engineers, and collaborate to turn hardware limits into shippable inference products.

As a Senior Engineer 2, you'll lead the design and delivery of high-scale AI Inference services, optimizing performance and mentoring junior engineers in a collaborative environment.

The Senior Engineer 2 will lead performance optimizations in AI Inference, focusing on architectural decisions, benchmarking, and complex GPU optimizations to enhance throughput and reduce latency for large models.

Build and run the Go-To-Market enablement function: design AE onboarding and certification, create pitch libraries, coach AEs via live/recorded calls and workshops, manage enablement content and cadence, and partner with RevOps, Product, PMM, Sales, and Marketing to keep sellers effective and informed.

Lead assessment and mitigation of security risks in the virtualization stack. Build threat models, rubrics, and defenses; partner with kernel, virtualization, and provisioning teams to design and implement mitigations; mentor engineers, manage vulnerability backlog, and promote security best practices across product teams.