About the Role
We are seeking a Member of Technical Staff, Performance Modeling to develop and apply performance models for our silicon and the full systems it operates within. The role focuses on building rigorous, decision-useful models that connect architectural choices to real end-to-end workload behavior.
You’ll work close with silicon architects and workload teams to explore design tradeoffs, validate performance assumptions, and identify bottlenecks early in the development cycle. This role is well-suited for engineers who enjoy reasoning from first principles, working with incomplete information, and co-exploring the design space as hardware and software evolve together.
This role will be performed onsite from one of our offices in Santa Clara, CA or Boston, MA.
Essential Duties & Responsibilities
Build and maintain performance models for our silicon, spanning individual components through full networked systems.
Evaluate architectural decisions using end-to-end ML workloads, translating model outputs into actionable guidance for the silicon team.
Work day-to-day with silicon architects, system designers, and workload owners to align performance expectations and constraints.
Develop frameworks and methodologies for performance verification, including sanity checks, model validation, and comparison against empirical data.
Identify performance bottlenecks, scaling limits, and sensitivity points across compute, memory, and interconnects.
Clearly communicate modeling assumptions, limitations, and conclusions to both technical and non-specialist stakeholders.
Qualifications
Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a closely related field.
5–10+ years of experience in performance modeling for computer architectures, accelerators, or high-performance networking systems.
Strong comfort working under uncertainty and iterating on models as designs and requirements evolve.
Solid understanding of modern machine learning workloads and how they map onto hardware systems.
Ability to reason across multiple abstraction layers, from architectural details to system-level performance behavior.
Preferred Qualifications
PhD in Electrical Engineering, Computer Engineering, or a related field.
Prior experience modeling performance for ML accelerators and/or ML-focused networking.
Familiarity with shared memory systems and frameworks (e.g. CUDA VMM).
Experience with scale-up and high-bandwidth interconnects (e.g. NVLink or similar technologies).
Compensation & Benefits
Competitive salary commensurate with experience including base salary, performance-based bonus, and early stage equity grant
Comprehensive benefits including health, dental, vision, and life insurance
Well-equipped, sunny offices in Santa Clara, CA and Boston, MA
Relocation assistance and visa sponsorship
Perks include a daily lunch stipend, 401k match, and more
A collaborative, continuous-learning work environment with smart, dedicated colleagues engaged in developing the next generation of architecture for high-performance computing
The Opportunity
Impact: We are tackling a fundamental challenge at the infrastructure layer: unlocking greater AI capability while dramatically improving efficiency. The work we do here compounds across state-of-the-art AI models, systems, and real-world applications.
Timing: Joining now means real ownership of the company and meaningful influence over product direction and execution. You’ll work from first principles, move quickly from insight to execution, and see your contributions directly reflected in what we build.
Culture: You’ll work alongside a group of people who care deeply about rigor, clarity, and impact. We value thoughtful disagreement, fast learning, and intellectual fearlessness. This is a place where strong ideas shine, curiosity is encouraged, and growth is a daily practice.
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