SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Tensordyne is an AI system solution company building high-performance, low-power generative AI inference systems through custom silicon, hardware, and software. The company enables multimodal generative AI inference acceleration at scale for hyperscaler and neocloud data center customers, with headquarters in Sunnyvale, CA and Munich, Germany.
You will work as a Simulation and Modeling Engineer on the functional model of Tensordyne's inference accelerator. This is a hands-on role for a strong software engineer with hardware knowledge—someone who writes maintainable code, is curious about how hardware works, and enjoys tackling hard problems.
Key responsibilities:
- Implement new architecture features and algorithms in the model
- Investigate discrepancies between model and hardware behavior
- Write tests, test plans, and test infrastructure for new features; track code coverage
- Document the model and validate it against architecture specifications and RTL
- Evaluate and adopt new modeling techniques
- Maintain and improve the codebase
- Work closely with architecture, hardware, and software teams
You'll need strong modern C++ skills, good knowledge of RTL and ASIC methodology, and solid understanding of hardware architecture. You should be comfortable with build systems and toolchains, able to take loosely specified features and deliver working, tested code with minimal guidance, and skilled at clear written and verbal communication across teams.
Nice-to-have skills include SystemC/TLM-2.0 knowledge, experience with AI/ML accelerators or GPU architectures, high-performance computing background, Python scripting, and familiarity with debugging and analysis tools (sanitizers, profilers, tracing).
Requirements:
- BS or higher in Computer Science, Computer Engineering, Electrical Engineering, or related field
- 3+ years of industry experience
- Strong modern C++
- Good knowledge of RTL and ASIC methodology
- Solid understanding of hardware architecture
- Good command of build systems and toolchains
- Ability to deliver working, tested code with minimal hand-holding
- Clear written and verbal communication; comfort working across teams
