Xnor.ai brings state-of-the-art artificial intelligence to the edge. Xnor's platform allows companies to run complex deep learning algorithms, formerly restricted to the cloud, locally on a range of devices including mobile phones, drones, and wearables. This new, highly scalable approach ensures complete privacy of data, eliminates the need for connectivity, and significantly reduces memory load and power demands, all without compromising accuracy or performance. Xnor is a venture-funded startup, founded on award-winning research conducted at the University of Washington and the Allen Institute for Artificial Intelligence. Xnor's industry-leading technology is used by global corporations in aerospace, automotive, retail, photography, and consumer electronics.
As a team, we are working to define and solve the hardest problems in computer vision and AI. Our roots are in research, which means at our core, we value learning, intellectual curiosity, and self-starters. We are proud to maintain a high engineering bar as our team scales, and it's clear to anyone that works with our code that our development process favors quality. Our Hardware Engineering team is relatively small (< 5), so your initiative and input will deliver meaningful impact to our platform. We stand by the importance of sticking with hard problems and working to solve them semi-autonomously at first. As a team, we are always willing to help but look for people who are committed to figuring things out.
As a startup, we never shoehorn people into fixed roles. At XNOR.AI ( http://xnor.ai/ ), there is no hard line between machine learning and engineering. We expect our engineers to understand how deep learning works and our research scientists to be able to code. This shared understanding results in deep collaborations that enable our team to rapidly develop novel models and optimized infrastructures of the highest production quality.
As a Hardware Engineer on the XNOR team, you will have the unique opportunity to be part of a startup team with close ties to the research community at the University of Washington and the Allen Institute for Artificial Intelligence. You will be responsible for writing high-performance, well-tested, power constrained Verilog code which then will be implemented on various FPGA platforms. In particular, you will focus on building novel high-performance, low-power FPGA and embedded platforms targeting machine learning applications. Our ideal team member is fearless when it comes to trying new things, is adept at reasoning about computer systems performance, and is willing to iterate on ideas. We value team members who can quickly prototype, iterating all the way to high-quality implementations.
- Design and test FPGA based systems for high-performance, low-power machine learning applications.
- Collaborate with multiple teams to specify the requirement of the design
- Understand project requirements and translate them into technical requirements to help build FPGA platforms.
- BS degree in Electrical Engineering or related field
- Experience in hardware programming languages, particularly Verilog
- Experience with embedded interface technologies: I2C, SPI, USB
- Deep knowledge of simulating and testing Verilog design
- Deep knowledge and experience on debugging Verilog designs before and after programming on an FPGA
- Masters Degree
- Experience in PCB designing
- Knowledge of high-speed system design
- Prior industry experience on FPGA development
- Deep knowledge of low power system design
- Knowledge of camera interfaces and imaging pipeline
Benefits and Perks
- Competitive salaries and stock options
- Comprehensive health care plan
- Visa petitioning
- Unlimited FTO
- Conference travel
- Culture of learning
- Healthy snacks and team lunches
- Regular team activities
- Standing Desks
- Waterfront views
XNOR.ai is proud to be an Equal Opportunity Employer.