Efficient Computer
Optimization Engineer
San Jose, CA OR Pittsburgh, PA · Senior
Sponsorship not specified$130k-$200kDetected 22 days ago
Machine LearningElectrical EngineeringSignal ProcessingCommunication
About the role
- Hands-on software development experience working closely with hardware, including exposure to at least one RISC, DSP or GPU platform.
- A passion for analyzing and maximizing software performance on an unique dataflow architecture.
Responsibilities
- Some background in performance profiling, benchmark design, or comparative hardware analysis.
Requirements
- A collaborative spirit, with the ability to work with and influence multiple engineering teams.
- Minimum Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
- Some experience with CUDA, HIP and/or other parallel programming models.
Compensation
- We offer a competitive salary for this role, generally ranging from $130,000 to $200,000, along with meaningful equity and comprehensive benefits.
Benefits
- We offer a competitive salary for this role, generally ranging from $130,000 to $200,000, along with meaningful equity and comprehensive benefits.
- Efficient offers a competitive compensation and benefits package, including 401K match, company-paid benefits, equity program, paid parental leave, and flexibility.
- This position is a unique opportunity to work on cutting-edge hardware/software co-design, while making an immediate impact in building the next generation of optimized libraries and applications used by our customers.
Company info
- We are seeking Software Optimization Engineers to join our growing team.
- We are committed to personal and professional development and strive to grow together as people and as a company.
- Our platform's unprecedented level of efficiency enables IoT devices to intelligently capture and curate first-party data to drive the next major computing revolution
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This listing is sourced directly from Efficient Computer's careers page and normalized into a canonical job model.