Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications. ABOUT THE ROLE This is a rare chance to help architect and lead the development of Sciforium’s next-generation model serving platform, the high-performance engine that will bring a multimodal, highly efficient foundation model to market. As a senior technical leader, you’ll not only build core components yourself but also guide and mentor other engineers, influencing engineering direction, standards, and execution quality. You will learn and shape the full AI stack: from GPU kernels and quantized execution paths to distributed serving, scheduling, and the APIs that power real-time AI applications. If you enjoy deep systems work, thrive on ownership, and want to lead engineers in building foundational AI infrastructure, this role puts you at the center of SciForium’s mission and growth. WHAT YOU'LL DO - Lead the technical direction of the model serving platform, owning architecture decisions and guiding engineering execution. - Build core serving components including execution runtimes, batching, scheduling, and distributed inference systems. - Develop high-performance C++ and CUDA/HIP modules, including custom GPU kernels and memory-optimized runtimes. - Collaborate with ML researchers to productionize new multimodal models and ensure low-latency, scalable inference. - Build Python APIs and services that expose model capabilities to downstream applications. - Mentor and support other engineers through code reviews, design discussions, and hands-on technical guidance. - Drive performance profiling, benchmarking, and observability across the inference stack. - Ensure high reliability and maintainability through testing, monitoring, and engineering best practices. - Troubleshoot and resolve complex issues across GPU, runtime, and service layers. IDEAL CANDIDATE PROFILE - Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience - 5+ years of experience designing and building scalable, reliable backend systems or distributed infrastructure. - Strong understanding of LLM inference mechanics (prefill vs decode, batching, KV cache) - Experience with Kubernetes/Ray, Containerization - Strong proficiency in C++, Python. - Strong debugging, profiling, and performance optimization skills at the system level. - Ability to collaborate closely with ML researchers and translate model or runtime requirements into production-grade systems. - Effective communication skills and the ability to lead technical discussions, mentor engineers, and drive engineering quality. - Comfortable working from the office and contributing to a fast-moving, high-ownership team culture. NICE-TO-HAVE - Experience with ML systems engineering, distributed GPU scheduling, open source inference engine like vLLM, Sglang, or TRT-LLM - Experience in building large scale ML/MLOps infrastructure - Proficiency in CUDA or ROCm and experience with GPU profiling tools - Experience at an AI/ML startup, research lab, or Big Tech infrastructure/ML team. - Familiarity with multimodal model architectures, raw-byte models, or efficient inference techniques. - Contributions to open-source ML or HPC infrastructure BENEFITS INCLUDE - Medical, dental, and vision insurance - 401k plan - Daily lunch, snacks, and beverages - Flexible time off - Competitive salary and equity EQUAL OPPORTUNITY Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Requirements

Must-haves

Lead the technical direction of the model serving platform, owning architecture decisions and guiding engineering execution.
Build core serving components including execution runtimes, batching, scheduling, and distributed inference systems.
Develop high-performance C++ and CUDA/HIP modules, including custom GPU kernels and memory-optimized runtimes.
Collaborate with ML researchers to productionize new multimodal models and ensure low-latency, scalable inference.
Build Python APIs and services that expose model capabilities to downstream applications.
Mentor and support other engineers through code reviews, design discussions, and hands-on technical guidance.
Drive performance profiling, benchmarking, and observability across the inference stack.
Ensure high reliability and maintainability through testing, monitoring, and engineering best practices.
Troubleshoot and resolve complex issues across GPU, runtime, and service layers.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
5+ years of experience designing and building scalable, reliable backend systems or distributed infrastructure.
Strong understanding of LLM inference mechanics (prefill vs decode, batching, KV cache)
Experience with Kubernetes/Ray, Containerization
Strong proficiency in C++, Python.
Strong debugging, profiling, and performance optimization skills at the system level.
Ability to collaborate closely with ML researchers and translate model or runtime requirements into production-grade systems.
Effective communication skills and the ability to lead technical discussions, mentor engineers, and drive engineering quality.
Comfortable working from the office and contributing to a fast-moving, high-ownership team culture.

Nice-to-haves

Experience with ML systems engineering, distributed GPU scheduling, open source inference engine like vLLM, Sglang, or TRT-LLM
Experience in building large scale ML/MLOps infrastructure
Proficiency in CUDA or ROCm and experience with GPU profiling tools
Experience at an AI/ML startup, research lab, or Big Tech infrastructure/ML team.
Familiarity with multimodal model architectures, raw-byte models, or efficient inference techniques.
Contributions to open-source ML or HPC infrastructure

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythonllmkubernetes

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k