Datacenter Agentic AI Performance Engineer (RISC-V)

Tenstorrent; United States; Remote; USD 100,000 - 500,000; Full time

Job summary

Tenstorrent is seeking a Datacenter & Agentic AI Workload Performance Optimization Engineer to optimize software workloads on our RISC-V platforms. You will identify bottlenecks across runtimes, compilers, and hardware interfaces, and implement software, compiler, runtime, and hardware-aware optimizations to improve throughput, latency, and efficiency in datacenter AI workloads.

The role sits remote, based in North America, with opportunities to influence future CPU architecture and AI

Qualifications

• Master's or PhD in Computer Engineering, Electrical Engineering, CS or related field.
• Hands-on experience with runtime or compiler optimization (OpenJDK/JIT, LLVM, GCC, V8, Python or equivalent)
• Strong understanding of CPU performance, memory hierarchies, concurrency, GC, vector/SIMD, and RISC-V.
• Experience with profiling tools (Linux perf, QEMU, tracing, performance modeling).
• Strong programming skills in Java, Python, C/C++, and RISC-V assembly.

Responsibilities

• Profile runtimes, compilers, and workloads to identify bottlenecks and inefficiencies.
• Develop software, compiler, runtime, and hardware-aware optimizations to improve throughput and latency.
• Tune major runtimes and profile real-world applications on RISC-V platforms.
• Explore vector/SIMD optimizations and custom ISA instructions for datacenter workloads.
• Collaborate across compiler, runtime, systems, hardware, and performance modeling teams.

Skills

Java

Python

C/C++

RISC-V assembly

Performance profiling

GC/concurrency

OpenJDK/JIT

LLVM

V8

Education

Master's or PhD in Computer Engineering / Electrical Engineering / Computer Science

Tools

OpenJDK/JIT

LLVM

GCC

V8

Python

QEMU

Linux perf

Tracing tools

RISC-V

Job description

Tenstorrent is seeking a Datacenter & Agentic AI Workload Performance Optimization Engineer to optimize software workloads on our RISC-V platforms. You will identify bottlenecks across runtimes, compilers, and hardware interfaces, and implement software, compiler, runtime, and hardware-aware optimizations to improve throughput, latency, and efficiency in datacenter AI workloads.

The role sits remote, based in North America, with opportunities to influence future CPU architecture and AI