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, Vulkan, and ROCm runtimes

Build runtime capabilities for model loading, batching, scheduling, caching, and distributed execution

Benchmark and diagnose correctness and performance problems across the inference stack

Contribute upstream to open-source projects such as llama.cpp and MLX

Benefits

Competitive salary and equity grants

Great medical, vision, dental healthcare plans

Catered team lunch / expensed dinners in the office

Flexible PTO

Flexible WFH

Sun-drenched office in SoHo in NYC