Software Engineer, Inference Runtime

Company: LM Studio

Location: New York, NY

Salary: $150,000 - $350,000 a year

Type: Full-time

Posted: 2026-08-07

About this role

LM Studio is used by millions of people around the world to run AI on their own computers, and now with Bionic - also in the cloud. Our values prioritize putting the human in the center, and creating tools that we want to use ourselves, and recommend to our friends and family.

As a team, we work with high technical intensity and personal responsibility. We are looking for curious, self-motivated, creative, and technically excellent teammates to join us and build the future of human-AI interactions in software.

The Role

We are looking for an Inference Runtime Software Engineer to push forward LM Studio's inference stack on-device and in the cloud. You will integrate new inference engines and runtime capabilities, bring up new open-weight models and modalities, and optimize model execution for a wide range of CPU and GPU targets. You will also contribute improvements to the open-source projects we build on.

Qualifications

  • Significant experience building production ML systems, inference runtimes, or performance-sensitive infrastructure
  • Strong programming ability in Python and C++
  • Deep understanding of transformer architectures and the mechanics of model inference
  • Experience profiling CPU or GPU workloads and reasoning about compute, memory, synchronization, and data movement
  • Experience with PyTorch and inference systems such as llama.cpp, MLX, ExecuTorch, vLLM, SGLang, or TensorRT-LLM
  • Strong debugging instincts across model code, runtime internals, operating systems, and CPU or GPU execution
  • Takes personal responsibility for the correctness and performance of their work

Bonus Qualifications

  • Past contributions to open-source inference runtime projects such as llama.cpp, MLX, ExecuTorch, vLLM, SGLang, or TensorRT-LLM

Responsibilities

  • Maintain and push forward our inference stack on-device and in the cloud
  • Bring up new model architectures and multimodal models
  • Improve latency, throughput, memory use, and reliability across CPU, CUDA, Metal, ...

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