Staff Software Engineer - ML Hardware Modeling
Lightmatter builds chips that enable extreme-scale artificial intelligence computing clusters. If you're a collaborative engineer or scientist who has a passion for innovation, solving challenging technical problems, and doing impactful work like building the world's first optical computers, consider joining the team at Lightmatter!
We are looking for talented software engineers to help us build our high-performance silicon-photonics-based network fabric for large-scale distributed systems. You will work closely with our software and hardware teams to define and optimize the features needed to accelerate the next generation of machine learning algorithms.
Lightmatter’s unique technology has the potential to alter the architecture of large-scale computers. In this role, you will be responsible for simulations and analysis of design concepts and planned hardware at multiple levels of scale and detail that will play a key role in the hardware-software codesign of future systems.
Responsibilities
- Design, development, maintenance of performance models for design concepts and planned hardware at both the subsystem and system levels
- Participate in the co-design process by working closely with both hardware and software teams by providing initial pathfinding analysis, developing early analytical models, and more detailed event-based simulations, and improving fidelity as designs progress
- Collaborate with machine learning and compiler teams to construct and evaluate the performance of machine learning workload.
- Master’s degree or a PhD with one year of experience in computer/software engineering, electrical engineering, or related field
- 5+ years of industry experience in hardware modeling, system simulation, computer architecture, or related fields
- Proficient with C++ and Python
- 2+ years of experience with modeling and large-scale distributed systems
Preferred Qualifications
- Familiarity with machine inference and training learning workloads
- Experience with modeling and analysis of machine learning accelerator architectures
- Proficient in a Linux-based environment
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