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Salary: USD 100,000 - 500,000 / annual
Tenstorrent is seeking a Workload Performance Optimization Engineer to optimize software workloads running on next-generation RISC-V platforms. You will work across modern datacenter and agentic AI workloads—including Java, Python, PHP, Node.js, Lua, Go, and Rust—to identify performance bottlenecks and develop software, compiler, runtime, and hardware-aware optimizations that improve throughput, latency, and efficiency.
This role sits at the intersection of software runtimes, compilers, CPU microarchitecture, and RISC-V silicon. Key responsibilities include:
- Bringing up and tuning major runtimes (OpenJDK/JIT, LLVM, GCC, V8, Python, etc.) on RISC-V platforms
- Profiling real-world applications to identify performance bottlenecks
- Investigating memory and concurrency behavior across complex systems
- Exploring optimizations using RISC-V Vector/Matrix capabilities and custom instructions
- Working with AI-assisted development and automated optimization workflows to accelerate performance engineering
- Collaborating across compiler, runtime, systems software, hardware, and performance modeling teams
- Influencing future CPU architecture by connecting real workload behavior to hardware design decisions
You will learn how to optimize modern software stacks from application and runtime down to CPU microarchitecture and silicon, how RISC-V capabilities accelerate datacenter and AI workloads, and how to build automated, AI-assisted performance optimization workflows.
The role is remote, based out of North America. Tenstorrent welcomes candidates at various experience levels; during the interview process, candidates will be assessed for the appropriate level, and offers will align accordingly.
**Requirements:**
- Master's or PhD in Computer Engineering, Electrical Engineering, Computer Science, or related field, with strong experience in performance optimization, computer architecture, compilers, or systems software
- Hands-on experience with runtime or compiler optimization (OpenJDK/JIT, LLVM, GCC, V8, Python, or equivalent)
- Strong understanding of CPU performance, memory hierarchies, concurrency, garbage collection, vector/SIMD optimization, and RISC-V architecture
- Expertise with performance profiling and analysis tools (Linux perf, runtime profilers, QEMU, tracing tools, performance modeling environments)
- Strong programming skills in Java, Python, C/C++, and RISC-V assembly, with ability to work across multiple software layers
- Experience optimizing software on RISC-V or another modern CPU architecture, with deep understanding of the hardware/software boundary
- Comfort profiling complex systems, forming hypotheses, and iterating through optimizations using data
- Excitement about emerging agentic AI development workflows and using AI tools to automate profiling, coding, benchmarking, and optimization
