Senior AI Infrastructure Engineer
Role Overview
We're looking for a Senior AI Infrastructure Engineer to help build and scale the systems powering next-generation AI inference. This is a hands-on engineering role with broad ownership across performance optimization, distributed systems, customer deployments, and AI infrastructure. You'll work directly with customers, influence product direction, and play a key role in ensuring real-world workloads perform at the highest level possible.
Key Responsibilities
- Improve performance across AI serving platforms, runtime environments, distributed systems, and low-level compute infrastructure.
- Build intelligent automation tools and AI-driven systems that streamline infrastructure and performance engineering work.
- Architect, deploy, and manage large-scale GPU and accelerator clusters spanning multiple hardware providers.
- Partner directly with customers to understand workload requirements, validate platform performance, and support production deployments.
- Lead technical evaluations using customer-specific workloads and communicate findings to engineering and ML teams.
- Monitor reliability, latency, throughput, and system health across production environments while driving issue resolution.
- Serve as the primary technical point of contact for assigned customers through deployment, optimization, and ongoing operation.
- Provide feedback from real-world usage to help guide future platform and product development.
Requirements
- Strong background building, scaling, and operating production software or infrastructure systems.
- Experience troubleshooting complex technical environments and diagnosing performance issues using profiling, monitoring, and observability tools.
- Ability to quickly learn unfamiliar technologies and take ownership of ambiguous technical problems.
- Experience working directly with customers, users, or external engineering teams in a technical capacity.
- Strong communication skills with the ability to explain complex technical concepts to highly technical audiences.
- Comfortable operating in fast-paced startup environments with a high degree of autonomy.
- Proven ability to move from loosely defined problems to practical solutions with minimal direction.
Preferred
- Experience with AI inference, machine learning infrastructure, GPU computing, or high-performance systems.
- Background working on distributed computing, orchestration platforms, compilers, runtimes, or systems performance optimization.
- Familiarity with accelerator technologies such as NVIDIA GPUs, TPUs, AMD, or other AI hardware platforms.
- Exposure to large-scale cloud infrastructure and production traffic environments.
- Experience supporting enterprise or high-growth startup customers running business-critical workloads.
Location: San Francisco, CA
FAQs
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