Efficient Computer
Performance Research Engineer (multiple levels)
About this role
Efficient is looking for Performance Research Engineers to innovate in energy-efficient computing by researching optimization techniques and collaborating with hardware and architecture teams. Candidates will help develop cutting-edge performance libraries and tools to enhance AI/ML capabilities.
What you'll do
- Research new optimization techniques for energy-efficient processors
- Design tools for AI-assisted optimization and performance analysis
- Collaborate with architecture and compiler teams
- Perform modeling experiments using architecture simulators
- Integrate new techniques into existing performance libraries
- Document complex systems and drive consensus across teams
What they're looking for
- Software development with hardware experience
- Performance issue analysis
- Framework and library design
- Familiarity with CUDA and parallel programming models
- Low-level programming in C/C++
- AI tools for code optimization
- Understanding of low-level programming interfaces
- Experience in performance profiling and benchmark design
Benefits
- Competitive salary between $180,000 and $250,000
- Equity program
- 401K match
- Company-paid benefits
- Paid parental leave
- Flexibility in work arrangements
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Efficient Computer
Efficient Computer builds ultra-low-power processors and the infrastructure needed to develop and optimize them, focusing on energy-efficient computing for AI/ML and general-purpose applications. The company is hiring hardware engineers, software optimization specialists, performance researchers, and infrastructure engineers to advance its processor design and benchmarking platform.
View all jobs at Efficient ComputerLikely interview questions
- Walk us through your experience optimizing performance on at least two different hardware platforms (RISC, DSP, or GPU). What were the key bottlenecks you identified and how did you address them?
- Describe your hands-on experience with parallel programming models like CUDA or HIP. What challenges did you encounter when optimizing code for resource-constrained environments?