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New York, New York, United States
Hybrid
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
