AI Inference Engineer

Be part of the team creating the software foundation for next-generation AI compute platforms. In this role, you’ll work across the full stack — from low-level kernels and hardware-optimized operators to large-scale ML deployment frameworks — in close collaboration with compiler developers, ML scientists, and hardware specialists. This position offers the chance to contribute to state-of-the-art AI infrastructure, fine-tune software for custom hardware, and deepen your expertise in system software and machine learning.

• →Design, develop, and maintain components of the deployment stack and software kernels for AI compute platforms
• →Optimize and implement core ML operators (e.g., GEMMs, convolutions, BLAS routines, SIMD kernels)
• →Translate computational graphs from ML frameworks onto the underlying hardware
• →Contribute to compiler infrastructure together with compiler and hardware teams
• →Investigate and resolve issues through system-level debugging and performance analysis
• →Deliver scalable software solutions under ambitious development schedules
• →Define and apply practices for testing, deployment, and scaling AI systems
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• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related discipline, with 3+ years of professional software development experience
• Solid knowledge of computer architecture, system software, data structures
• Strong programming skills in C/C++ or Python in Linux environments using common development tools
• Hands-on experience implementing algorithms in high-level languages (C/C++/Python)
• Exposure to specialized hardware (GPUs, FPGAs, DSPs, AI accelerators) and frameworks such as OpenCL or CUDA
• Experience designing or working with high-performance software systems
• Solid knowledge of ML fundamentals
• Motivated team player with a strong sense of responsibility

• Model serving frameworks (e.g., Triton Inference Server, DeepSpeed Inference, vLLM)
• Deep learning frameworks (e.g., PyTorch, TensorFlow)
• ML runtimes (e.g., ONNX Runtime, TVM, IREE, XLA)
• Distributed collectives (e.g., Gloo, MPI)
• Software testing and validation methodologies
• Deploying ML workloads (LLMs, VLMs, NLP, etc.) across distributed systems
• Implementation of ML operators and kernels (e.g., SIMD routines, Activation functions, Pooling layers, Quantization layers)
• Hardware-aware optimizations and performance tuning
• 2+ years of experience developing software targeting AI hardware
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Contribution to open-source projects (e.g., LLVM, PyTorch, TensorFlow, ONNX Runtime, xDSL, IREE) is a big plus.