Job description

Our Client, an Internet Content & Information company, is looking for a Software Engineer III for their Redmond, WA location. Responsibilities: • The ideal candidate is equally comfortable in a PyTorch training loop and in a C/C++ inference runtime on a resource-constrained device. • They understand that on-device ML is a co-design problem, where model architecture, quantization, and the target hardware all shape one another, and they can make the trade-offs required to get a research model running on real hardware quickly. Familiarity with audio ML, computer vision ML, or audio DSP is a strong plus, as many of our models operate on multimodal (audio and visual) sensor data. • Our team cultivates an honest and considerate environment where self-motivated individuals thrive. We encourage a strong sense of ownership and embrace the ambiguity that comes with working on the frontiers of research. • Responsibilities • Work side by side with research scientists to take ML models from research prototypes to efficient, real-time inference on embedded devices • Train, fine-tune, and adapt ML models with deployment constraints (latency, memory, power, compute) in mind • Optimize models for on-device inference using techniques such as quantization, pruning, distillation, operator fusion, and architecture adjustments • Port, integrate, and profile ML models on embedded targets (e.g. DSPs, NPUs, microcontrollers, mobile SoCs), identifying and resolving performance bottlenecks • Write clean, maintainable, high-performance C/C++ code for on-device inference pipelines and integrate it into real-time prototype systems • Build tools and infrastructure that make it faster for researchers to evaluate model performance and accuracy on target hardware • Coordinate with internal hardware, runtime, and toolchain teams across Client and Client to evaluate and integrate with existing systems • Work collaboratively as part of a multifunctional team where communication, documentation, and teamwork are highly valued • Learn constantly, dive into new areas with unfamiliar technologies, and embrace the ambiguity of AR/VR problem solving Requirements: • BS degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent work experience • Industry experience optimizing and running ML inference on embedded or resource-constrained devices • Experience training ML models using deep learning frameworks such as PyTorch or TensorFlow • 2+ years of industry experience writing C/C++ • Experience profiling and debugging performance issues across the ML model and the target hardware • Experience working in cross-functional teams • Preferred qualifications • Experience with ML for audio (e.g. speech enhancement, source separation, keyword spotting, acoustic event detection) • Experience with ML for computer vision (e.g. detection, tracking, segmentation, pose estimation) • Experience with audio DSP fundamentals (e.g. filtering, STFT/filterbanks, beamforming, real-time audio processing) • Experience with on-device inference frameworks and toolchains (e.g. ExecuTorch, TFLite/LiteRT, ONNX Runtime, vendor NPU/DSP SDKs) • Experience with model compression techniques such as quantization-aware training, post-training quantization, pruning, or distillation • Experience writing hardware-specific optimizations (e.g. SIMD/NEON, DSP intrinsics, fixed-point arithmetic) • Experience working in the AR/VR or wearable device space • Ability to gather requirements and work closely with researchers to develop novel solutions • History of writing code to support the execution of research initiatives • Top 3 must-have hard skills: • Optimizing and running ML inference on embedded / resource-constrained devices • Training ML models (PyTorch or similar) • C/C++ (2+ years industry experience) • Good to have skills: • Audio ML • Computer vision ML • Audio DSP Why Should You Apply? • Health Benefits • Referral Program • Excellent growth and advancement opportunities

Requirements

Must-haves

Our Client, an Internet Content & Information company, is looking for a Software Engineer III for their Redmond, WA location.
The ideal candidate is equally comfortable in a PyTorch training loop and in a C/C++ inference runtime on a resource-constrained device.
Our team cultivates an honest and considerate environment where self-motivated individuals thrive. We encourage a strong sense of ownership and embrace the ambiguity that comes with working on the frontiers of research.
Work side by side with research scientists to take ML models from research prototypes to efficient, real-time inference on embedded devices
Train, fine-tune, and adapt ML models with deployment constraints (latency, memory, power, compute) in mind
Optimize models for on-device inference using techniques such as quantization, pruning, distillation, operator fusion, and architecture adjustments
Port, integrate, and profile ML models on embedded targets (e.g. DSPs, NPUs, microcontrollers, mobile SoCs), identifying and resolving performance bottlenecks
Write clean, maintainable, high-performance C/C++ code for on-device inference pipelines and integrate it into real-time prototype systems
Build tools and infrastructure that make it faster for researchers to evaluate model performance and accuracy on target hardware
Coordinate with internal hardware, runtime, and toolchain teams across Client and Client to evaluate and integrate with existing systems
Work collaboratively as part of a multifunctional team where communication, documentation, and teamwork are highly valued
Learn constantly, dive into new areas with unfamiliar technologies, and embrace the ambiguity of AR/VR problem solving
BS degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent work experience
Industry experience optimizing and running ML inference on embedded or resource-constrained devices
Experience training ML models using deep learning frameworks such as PyTorch or TensorFlow

Nice-to-haves

Experience with ML for audio (e.g. speech enhancement, source separation, keyword spotting, acoustic event detection)
Experience with ML for computer vision (e.g. detection, tracking, segmentation, pose estimation)
Experience with audio DSP fundamentals (e.g. filtering, STFT/filterbanks, beamforming, real-time audio processing)
Experience with on-device inference frameworks and toolchains (e.g. ExecuTorch, TFLite/LiteRT, ONNX Runtime, vendor NPU/DSP SDKs)
Experience with model compression techniques such as quantization-aware training, post-training quantization, pruning, or distillation
Experience writing hardware-specific optimizations (e.g. SIMD/NEON, DSP intrinsics, fixed-point arithmetic)
Experience working in the AR/VR or wearable device space
Ability to gather requirements and work closely with researchers to develop novel solutions
History of writing code to support the execution of research initiatives

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Effort: medium

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