Location: Bristol, Cambridge, Edinburgh

Role Overview:

Early stage start up specialising in AI chip design

Key Responsibilities:

• Collaborate with our team to design, develop, and optimize a new chip architecture for our AI processor.
• Conduct research and analysis to identify cutting-edge trends, techniques, and technologies in the field.
• Develop and implement innovative solutions to address power, performance, and area (PPA) challenges.
• Evaluate and optimize algorithms and models for implementation in hardware.
• Collaborate with software and hardware engineers to ensure seamless chip integration with other system components.
• Create and maintain design documentation, including specifications, block diagrams, and interface definitions.
• Support prototype testing, debugging, and performance evaluation.

Experience Required:

• BS / MS / PhD in Electrical Engineering, Computer Engineering, or a related field.
• Excellent academic record in integrated circuit (IC) design, ideally focusing on AI accelerators or related technologies.
• Proficiency in ASIC or FPGA design flow, RTL design, and verification (using Verilog)
• Strong understanding of digital circuit design and computer architecture.
• Familiarity with neural network algorithms and computational models
• Experience with industry-standard EDA tools (e.g., Vivado or Cadence, Synopsys, or Mentor Graphics)
• Strong problem-solving skills and the ability to work independently and collaboratively
• Excellent communication and interpersonal skills

Nice-to-have:

• Publications or patents in GPU / AI accelerator design or related fields
• Experience with FPGA prototyping and emulation
• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch)

Whats Offered:

• Salary : Competitive and commensurate with experience and qualifications
• Equity : Stock options in our early-stage startup, providing significant potential for financial growth as the company succeeds
• Bonus : Performance-based annual bonuses, subject to company and individual performance metrics