About Recogni:

Artificial intelligence (AI) is transforming our world. It can perform cognitive functions that previously only humans could do, such as perceiving interactions across different environments with the ability to quickly learn and then solve complex problems. Recogni is a system solution company that specializes in the design of industry-leading high-performance, low-power AI inferencing. Our mission is to enable multimodal Generative AI inference acceleration at scale by providing safe, sustainable, high-performance AI-driven solutions for many markets. We are at the leading edge of advancing the latest research and product improvements for Al inference solutions that will make Al even more advantageous for compelling new applications. Recogni is a well funded, fast-paced startup company with headquarters in both San Jose, CA, and Munich, Germany. We also have many talented team members working remotely. We prioritize our employees' well-being and their families, aiming for a healthier, happier life inside and outside work. We value their contributions and offer tailored benefits for health and financial security, catering to different life stages. Our comprehensive benefits and competitive compensation, including flexible spending and Bonusly awards, reflect our commitment to a supportive and inspiring work environment.

About the role:

To keep pace in this exciting, multi-disciplinary field, we're looking for experienced systems architects with a deep expertise in architectural modeling via functional, performance and power simulations that will guide the creation of our next-generation Al hardware and software systems. The successful candidate will have passion for Al, with outstanding analytical skills. If that's you, then we'd love to talk to you! 

Where you'd help us: 

  • Deep engagement with silicon, systems hardware and software teams to understand functionality, accuracy, performance and power efficiency goals, as needed to build architectural simulator designs that help holistically guide optimal architectural decisions within evolving constraints. 
  • Join and substantially contribute to an architecture team that's creating a modern Al inference modeling simulation and analysis framework in C++/SystemC, with representative Al inference workloads that will help guide our decisions about workload sharing across complex systems, as well as hardware-software partitioning/co-design, Al inference algorithm evaluation and enablement of silicon level exploration with quantitative profiling across key performance, accuracy and power metrics. The Al inference simulation models' scope will range from the microarchitectural silicon level to full system level, across a variety of multimodal Al inference networks. 
  • Establish and automate the tracking of key Al inference workloads across key metrics for simulators that you will help create, with helpful profilers and visualizations that drive continuous improvements in speed, accuracy and productivity, within modern build/regression/test methodologies. 
  • You will lead many aspects of the full life-cycle of performance modeling, from early architectural exploration to post-silicon, and full system Al verification and correlation. Therefore an understanding of complexities involved in the development, debugging, correlation and optimization of virtual prototype models for complex SOCs and software, along with the inherent tradeoffs in speed versus accuracy of such simulation models is highly desired.


  • Strong C++ and SystemC programming skills, with hands-on experience creating simulation environments and workload generation for cycle-accurate, bit-accurate, and transaction accurate models for algorithm evaluation, system performance/power analysis and hardware/software partitioning and verification using a combination of modern EDA and custom built tools. 
  • Prior experience with high performance machine learning & high bandwidth network systems is highly desired, along with a basic understanding of modern Al architectures. 
  • The successful candidate will also possess outstanding skills in technical communication, engineering collaboration, and a passion for technology innovation. 
  • An MS or PhD in CS, EE, Computer Engineering, or related field and/or equivalent experience. 
  • The work location of San Jose or Munich is ideal, but working from a remote home office is possible for highly experienced candidates - as long as there's a willingness to occasionally travel to engineering offices when needed. 
Reasons to consider joining Recogni:
  • Ground floor opportunity with the team; be part of shaping one of the most exciting new companies.
  • Learning and development opportunities from a highly diverse and talented peer group, including experts in a wide range of fields, from Artificial Intelligence & Computer Vision to Systems & Device Engineering.
  • Competitive benefits package including Medical, Vision, Dental
  • Perks including meals, snacks, drinks and us!
  • Sharp, motivated co-workers in a fun office environment
  • Employee Stock Purchase Plan
  • Flexible work hours & generous PTO policies

Recogni is an equal opportunity employer. We believe that a diverse team is better at tackling complex problems and coming up with innovative solutions. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.

A note to Recruitment Agencies: Please don’t reach out to Recogni employees or leaders about our roles -- we’ve got it covered. We don’t accept unsolicited agency resumes and we are not responsible for any fees related to unsolicited resumes. Thank you for your understanding.

Apply for this Job

* Required
resume chosen  
(File types: pdf, doc, docx, txt, rtf)
cover_letter chosen  
(File types: pdf, doc, docx, txt, rtf)

Enter the verification code sent to to confirm you are not a robot, then submit your application.

This application was flagged as potential bot traffic. To resubmit your application, turn off any VPNs, clear the browser's cache and cookies, or try another browser. If you still can't submit it, contact our support team through the help center.