Audio ML Engineer III — Redmond, WA

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.