AI Infrastructure Performance Lead

Renice AI; Mountain View (CA); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Renice AI is seeking a Performance Lead to drive architecture decisions for AI inference workloads across the full system stack in Mountain View, CA. You will build modeling frameworks, analyze tradeoffs across compute, memory, networking, and storage, and translate results into actionable guidance for internal teams and hardware partners.

This role leads a small team of engineers, collaborates with ML, systems, and hardware groups, and shapes reference designs and long-term infrastructure

Qualifications

• Experience owning or building performance modeling frameworks used to drive real system design decisions.
• Deep knowledge of AI and machine learning workloads, including training and/or inference at scale.
• Understanding of system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
• Comfort working across abstraction layers, from workload behavior to hardware implementation.
• Experience using analytical modeling or simulation to inform architectural decisions.
• Ability to operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
• Clear communication and the ability to influence internal teams and external partners.

Responsibilities

• Build and own a performance modeling framework and toolchain to evaluate AI systems across multiple levels of abstraction.
• Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.
• Develop performance models to guide decisions on scale-up versus scale-out architectures, interconnect and network design, and memory hierarchy and system balance.
• Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.
• Influence reference designs and vendor roadmaps through data-driven insights.
• Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.
• Lead and grow a small team of two to three engineers, setting technical direction and maintaining high standards for modeling rigor.
• Continuously improve modeling fidelity by validating against real system behavior and measurements.

Skills

Performance modeling

AI workloads

System tradeoffs

Analytical modeling

Cross-functional communication

Job description

Renice AI develops system and infrastructure solutions designed for the unique demands of advanced AI inference workloads. We work closely with external research, software, and hardware partners to shape the next generation of AI systems, from silicon to model weights through full-scale deployments.

This role focuses on understanding and optimizing performance across the full system stack, ensuring that architectural decisions are grounded in rigorous, quantitative analysis of real-world workloads.

About the role

We are seeking a Performance Lead to answer forward-looking architectural questions across AI infrastructure systems.

You will develop modeling frameworks and methodologies to evaluate system-level tradeoffs and guide key design decisions. Your work will directly influence reference architectures, vendor designs, and long-term infrastructure strategy.

This role sits at the intersection of AI workloads, system architecture, and quantitative modeling. It requires strong technical judgment, ownership, and the ability to translate complex analysis into clear, actionable guidance.

Key responsibilities

• Build and own a performance modeling framework and toolchain to evaluate AI systems across multiple levels of abstraction.
• Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.
• Develop performance models to guide decisions on scale-up versus scale-out architectures, interconnect and network design, and memory hierarchy and system balance.
• Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.
• Influence reference designs and vendor roadmaps through data-driven insights.
• Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.
• Lead and grow a small team of two to three engineers, setting technical direction and maintaining high standards for modeling rigor.
• Continuously improve modeling fidelity by validating against real system behavior and measurements.

Qualifications

• Experience owning or building performance modeling frameworks used to drive real system design decisions.
• Deep knowledge of AI and machine learning workloads, including training and/or inference at scale.
• Understanding of system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
• Comfort working across abstraction layers, from workload behavior to hardware implementation.
• Experience using analytical modeling or simulation to inform architectural decisions.
• Ability to operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
• Clear communication and the ability to influence internal teams and external partners.

Preferred skills

• Experience working with hardware vendors across silicon, networking, and system design.
• Background in data center infrastructure or hyperscale systems.
• Familiarity with accelerators and high-performance interconnects.
• Experience influencing hardware roadmaps or reference architectures.
• Prior experience leading or mentoring engineers.

About Renice AI

Renice AI builds the integrated stack for AI inference: the model, the software and the hardware designed as one system, rather than one layer at a time.

Nobody designed the AI stack as a whole. Models, software, chips, memory and networking are each owned by a different part of the industry, and each locked in choices that were rational at the time but were never made for serving. Added up, those choices cap how many tokens the world can make. We redesign across them so the machines the world already has produce more tokens in the datacenter and on the desktop.