Staff AI Solution Engineer - FPGA

Cognichip; Redwood City (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Cognichip is looking for a Staff AI Solution Engineer – FPGA to connect AI solutions with hardware design teams. This role involves applying AI to real-world problems and collaborating with design teams to optimize FPGA-based workflows. The ideal candidate will have a Master’s degree in Electrical Engineering, extensive experience in FPGA design, and strong communication skills.

Key responsibilities include supporting customer integration of ACI solutions, providing training sessions, and creating technical documentation.

Qualifications

• 7 to 10 years of experience in FPGA-based RTL design, verification, and optimization.
• Excellent written and verbal communication skills.
• Experience with machine learning or deep learning concepts is a strong advantage.

Responsibilities

• Serve as the primary technical point of contact for customers.
• Collaborate closely with customer design teams, providing hands-on support.
• Develop and deliver technical presentations and training sessions.
• Analyze design flows and diagnose customer-specific issues.
• Create clear, concise technical documentation and reference designs.

Skills

Verilog/SystemVerilog

FPGA toolchains (AMD/Altera)

Python

Technical communication

Problem-solving mindset

Education

Master’s degree in Electrical Engineering

Job description

Job Summary

At Cognichip, we’re not just building AI; we’re enabling our customers to harness the power of AI to transform their silicon design workflows. As an AI Application Engineer with an FPGA focus, you’ll be the crucial link between our groundbreaking Artificial Chip Intelligence (ACI) solutions and the hardware design teams who will use them to target high-performance FPGA platforms. You’ll translate complex technical concepts into practical solutions, helping teams successfully adopt our solutions and unleash their creativity on programmable logic. If you thrive on solving technical challenges and empowering others to build the future of accelerated computing, this is your stage.

Job Title

Staff AI Solution Engineer – FPGA

Key Responsibilities

• Apply AI to real-world problems. Serve as the primary technical point of contact for customers, providing expert guidance and support to help them integrate Cognichip ACI® solutions into their FPGA-based design flows and hardware acceleration pipelines.
• Bridge the gap. Collaborate closely with customer design teams, providing hands‑on support for RTL development, optimization, and hardware troubleshooting to ensure a seamless experience when going from innovative idea to reconfigurable chips. You’ll also act as the voice of the customer, relaying critical feedback to our AI science and software teams.
• Drive adoption and success. Develop and deliver technical presentations, hardware demonstrations, and training sessions that showcase the value and capabilities of ACI for the FPGA hardware and software ecosystems.
• Diagnose and solve. Dive deep into customer‑specific issues—analyzing design flows, log files, and user interactions—to diagnose problems, provide solutions, and help improve our products and services.
• Educate and inform. Create clear, concise technical documentation, application notes, and reference designs for FPGA implementation to empower our customers and accelerate their learning curve.

Required Qualifications

• Master’s degree in Electrical Engineering, or a closely related field.
• 7 to 10 years of experience in FPGA-based RTL design, verification, and optimization, with a passion for customer‑facing roles.
• Proficiency in Verilog/SystemVerilog, FPGA toolchains (AMD/Altera), and scripting languages like Python.
• Excellent written and verbal communication skills, with a strong ability to translate complex technical information into clear, actionable guidance.
• A consultative, problem‑solving mindset and a passion for helping others succeed.
• Demonstrated knowledge of the full FPGA design process, from RTL to synthesis and timing closure.
• Experience with machine learning or deep learning concepts is a strong advantage.