Staff AI Engineer, Inference & Optimization at Sonatus

Candidates must have a minimum of 7 years of work experience in MLOps or a similar role with a strong focus on high-performance machine learning systems. Proven experience with inference optimization techniques such as quantization, pruning, and model distillation is required, along with deep hands-on experience with hardware acceleration for machine learning, including familiarity with GPUs, TPUs, NPUs and related software ecosystems. Strong experience with AI compilers and runtime environments like TensorRT, OpenVINO, and TVM is necessary. Proven experience deploying and managing ML models on edge devices, strong experience in designing and building distributed systems, and proficiency with inter-process communication protocols like gRPC, message queuing systems like MQTT, and efficient data handling techniques such as buffering and callbacks are essential. Hands-on experience with popular ML frameworks such as PyTorch, TensorFlow, TFLite, and ONNX, proficiency in programming languages including Python and C++, and a solid understanding of machine learning concepts, the ML development lifecycle, and the challenges of deploying models at scale are required. Proficiency with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure), and expertise in CI/CD principles and tools applied to machine learning workflows are also necessary. A Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field is required.