Product Performance Engineer

Hark; San Jose (CA); On-site; USD 120,000 - 300,000; Full time

Job summary

Hark is searching for a skilled professional to analyze workload behavior and model system performance for cutting-edge AI hardware and software systems.

The ideal candidate will have over 5 years of experience in workload analysis, along with a BS in Computer Science or Electrical Engineering, and strong programming skills.

This full-time position offers an annual salary range of $120,000 to $300,000, depending on experience and qualifications.

Qualifications

• 5+ years analyzing workload behavior and modeling system performance.
• Hands-on experience with profiling, benchmarking, and tracing tools.
• Demonstrated ability to translate quantitative analysis into recommendations.

Responsibilities

• Build understanding of target workloads and their demands.
• Model performance across silicon, system, and user experience.
• Collaborate with different teams to align assumptions and deliver insights.

Skills

Analyzing workload behavior

Modeling system performance

Strong programming skills in Python

Experience with systems language (C/C++/Rust)

Familiarity with profiling tools

Education

BS in Computer Science or Electrical Engineering

Tools

Profiling tools

Benchmarking tools

Tracing tools

Job description

About Hark

Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.

We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.

To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.

About the Role

• Build deep understanding of target workloads and characterize their compute, memory, bandwidth, and latency demands on a variety of target hardware platforms
• Model performance at every level — silicon, system, service, and end-to-end user experience — and connect insights across layers
• Develop and maintain analytical and simulation-based models that project system performance across design and assumption variants
• Collaborate with silicon, software, hardware, model, and service teams to align assumptions and deliver insights
• Validate models against measured workload behavior and continuously improve fidelity
• Translate findings into clear, actionable recommendations for architects, engineers, and leadership
• Proactively identify performance risks and design opportunities ahead of architectural commit points

Requirements

• 5+ years analyzing workload behavior and modeling system performance for consumer or data center products
• BS in Computer Science, Electrical Engineering, or related technical discipline
• Hands-on experience with profiling, benchmarking, and tracing tools across the stack
• Strong programming skills in Python and at least one systems language (C/C++/Rust)
• Demonstrated ability to translate quantitative analysis into architectural recommendations

Bonus Qualifications

• MS or PhD in a relevant technical discipline
• Experience with hardware/software co-design and architectural exploration tooling
• Familiarity with accelerator, SoC, or memory subsystem architecture
• Comfort moving fluidly between rigorous quantitative work and cross-functional influence
• Curiosity, technical depth, and a bias toward learning new system domains quickly

Compensation

The US base salary range for this full-time position is between $120,000 - $300,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.