VP, Software Engineering at Fluidstack

Fluidstack

San Francisco, CA·1d ago

ExecutiveFull-timeRustAWSGCPAzureKubernetesGoAI

Salary Range

$285,600 - $386,400 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

We are looking for a VP of Software Engineering to join Fluidstack, a company building the infrastructure for abundant intelligence. This role will own the full software and SRE organizations responsible for our managed orchestration (Kubernetes and SLURM) offerings as well as our managed inference services.

Key Responsibilities

→Own and scale the engineering organization across managed Kubernetes and SLURM, as well as our managed inference product, including Software Engineers and SREs across all three product areas.
→Set the technical and architectural roadmap for cluster orchestration and AI inference serving, from bare-metal provisioning through control-plane design and developer-facing APIs.
→Drive reliability, performance, and scalability standards across the stack, owning SLAs for customers running production AI training and inference workloads on Fluidstack infrastructure.
→Partner closely with Product, Sales, and Customer Success to translate customer needs from top AI labs and enterprises into concrete engineering investments and prioritization decisions.
→Establish engineering culture, hiring bar, and operational practices that attract and retain exceptional talent in a competitive market.
→Remain hands-on at the level of design reviews, architecture decisions, and critical incident response, maintaining deep technical credibility with the team.
→Build and maintain a high-trust, high-accountability team environment where engineers own outcomes end-to-end, from design through production operations.

Requirements

✓10+ years of software engineering or systems engineering experience, with at least 4 years managing engineering teams including both Software Engineers and SREs.
✓Deep hands-on experience with Kubernetes and SLURM in production environments, including scheduling internals, resource management, and multi-tenant cluster operations.
✓Strong background in bare-metal infrastructure and GPU/accelerator systems, including server imaging, networking (InfiniBand/RoCE), firmware, and hardware lifecycle management.
✓Demonstrated ability to build and scale AI inference serving infrastructure, including familiarity with inference optimization techniques (quantization, continuous batching, speculative decoding, KV cache management).
✓Track record of building and growing high-performing engineering organizations of 40+ engineers across complex, cross-functional domains.
✓Strong communicator who can represent technical strategy to executive leadership, customers, and board-level stakeholders.