Audio ML Engineer III

Job Title: Audio ML Engineer III Location: Redmond, WA (Onsite) Duration: 12-Month Contract This is an exciting opportunity to work on next-generation wearable, audio, and intelligent device technologies while helping transform innovative research into impactful products. Responsibilities Collaborate with researchers and engineers to transition machine learning models from prototype to deployment. Train, fine-tune, and adapt deep learning models with real-world deployment constraints in mind. Optimize models for on-device inference through techniques such as quantization, pruning, distillation, and architecture optimization. Deploy, integrate, and profile ML models on embedded hardware platforms. Develop high-performance C/C++ software for inference pipelines and real-time systems. Analyze and resolve performance bottlenecks related to latency, memory consumption, and power efficiency. Build tools and infrastructure that accelerate model evaluation and deployment workflows. Partner with cross-functional teams to integrate machine learning capabilities into embedded systems. Document technical solutions and contribute to best practices across the engineering organization. Stay current with advancements in machine learning, embedded computing, and edge AI technologies. Required Qualifications Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience. Experience deploying and optimizing machine learning models on embedded or resource-constrained devices. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Minimum 2 years of professional C/C++ development experience. Experience profiling and debugging performance issues across software and hardware environments. Ability to work effectively within cross-functional engineering teams. Preferred Qualifications Experience with audio-related machine learning applications such as speech enhancement, source separation, keyword spotting, or acoustic event detection. Experience with computer vision applications including detection, tracking, segmentation, or pose estimation. Understanding of audio signal processing concepts and real-time processing systems. Experience with model compression and optimization techniques. Familiarity with embedded AI deployment frameworks and edge inference runtimes. Experience optimizing software for specialized hardware accelerators or low-power devices. Background in wearable devices, AR/VR technologies, or edge computing environments. Strong problem-solving skills and ability to work in research-driven, fast-paced environments.