Product Manager - AI SW Infrastructure (AI Software Stack / Compiler / Platform) at Majestic Labs

Job description

Description We’re a fast-moving, US-Israeli AI startup building next-generation infrastructure for the world’s most demanding AI workloads. Our mission is to accelerate the future of intelligence and make it ubiquitously accessible by delivering full stack custom AI servers optimized for large-scale inference and training. Backed by top-tier investors and led by industry veterans, we’re scaling rapidly. We seek forward-thinking engineers and operators who thrive in a collaborative environment and excel at solving complex challenges from first principles. If you want to build infrastructure that fundamentally changes what the world can accomplish with AI, come join us! About the position We are building the next generation of AI infrastructure-from custom silicon to the servers and systems that power the world's largest AI models. Our vertically integrated approach enables us to optimize every layer of the AI compute stack, delivering breakthrough performance, efficiency, and scalability. We’re looking for a visionary, highly technical Senior Product Manager who thrives at the intersection of data center infrastructure, server-grade hardware and software, systems engineering, and AI to lead our AI software stack, compiler platform, and server system software. This role is responsible for defining and delivering the software ecosystem that makes Majestic's system completely seamless for developers and cloud infrastructure teams. You will own the compiler roadmap, runtime engines, and framework integrations that translate standard deep learning model representations into highly optimized execution paths on the Ignite APU. Responsibilities • Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions. • Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations. • Compiler Pipeline and Toolchain Roadmap: Define requirements for the software and AI model compilation toolchain • Framework Integration and Interoperability: Lead product partnerships and technical integrations with major machine learning frameworks and runtimes, ensuring native support for PyTorch, JAX, vLLM, SGLang, and Hugging Face Optimum without requiring customer code modifications. • High-Performance Kernel Optimization Roadmap: Define performance optimization roadmap for low-level operator libraries, including BLAS, custom OpenAI Triton kernels • Scale-Out and Clustering Orchestration: Define requirements for clustering and multi-node scale-out configurations, ensuring seamless execution across scale-out setups utilizing SLURM, Ray, LMCache, etc. • Developer Platform Usability: Act as the primary advocate for developer experience, monitoring how software compiles and executes in customer sandboxes, and proactively removing the need for manual memory management scripts, custom compiler hacks, or API wrappers. Requirements • Education: Master of Science (M.Sc.) or Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics is a strict, non-negotiable requirement. • AI Software Stack Expertise: Minimum of 5 to 8 years of experience in product management or software systems engineering, with a proven track record of shipping compiler technologies, deep learning frameworks, or developer platform tools. • Parallel Programming Fluency: Deep theoretical and practical understanding of CUDA, Triton, high-performance C++ DSP libraries, and multi-threaded parallel computation paradigms. • Scale-Out Infrastructure Knowledge: Demonstrated familiarity with HPC scale-out infrastructures, distributed memory architectures, and networking protocols designed for high-performance clustering. • Prior experience at accelerated software, cloud, or startup organizations building complex AI compute systems, such as NVIDIA, Meta, Google, AWS, SambaNova, Cerebras, Groq, or Lightning AI. Preferred Qualifications • MBA or equivalent experience in technology commercialization or product strategy. • Active contributions to open-source AI infrastructure projects, compilers (MLIR/LLVM), or performance optimization libraries. • Direct experience working inside a frontier AI lab or foundation model organization (e.g., OpenAI, Anthropic, xAI, Google DeepMind) developing large-scale distributed systems. We seek smart, curious, self-driven individuals who enjoy creative problem-solving and continuous learning. Even if you don't meet all requirements, we'd love to hear from you if you have the mindset to tackle complex challenges. Majestic Labs is an equal opportunity employer deeply committed to diversity of background and thought. If you are ready to dare greatly, learn from mistakes, engage in vigorous debates, and help create a step-function shift in human technical capability, we want to meet you!

Requirements

Must-haves

Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions.
Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations.
Compiler Pipeline and Toolchain Roadmap: Define requirements for the software and AI model compilation toolchain
High-Performance Kernel Optimization Roadmap: Define performance optimization roadmap for low-level operator libraries, including BLAS, custom OpenAI Triton kernels
Scale-Out and Clustering Orchestration: Define requirements for clustering and multi-node scale-out configurations, ensuring seamless execution across scale-out setups utilizing SLURM, Ray, LMCache, etc.
Education: Master of Science (M.Sc.) or Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics is a strict, non-negotiable requirement.
Parallel Programming Fluency: Deep theoretical and practical understanding of CUDA, Triton, high-performance C++ DSP libraries, and multi-threaded parallel computation paradigms.
Scale-Out Infrastructure Knowledge: Demonstrated familiarity with HPC scale-out infrastructures, distributed memory architectures, and networking protocols designed for high-performance clustering.
Prior experience at accelerated software, cloud, or startup organizations building complex AI compute systems, such as NVIDIA, Meta, Google, AWS, SambaNova, Cerebras, Groq, or Lightning AI.

Nice-to-haves

MBA or equivalent experience in technology commercialization or product strategy.
Active contributions to open-source AI infrastructure projects, compilers (MLIR/LLVM), or performance optimization libraries.
Direct experience working inside a frontier AI lab or foundation model organization (e.g., OpenAI, Anthropic, xAI, Google DeepMind) developing large-scale distributed systems.

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Keywords to mirror

aimachine learningapiaws

Platform: comeet
Effort: medium

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