Position: Software Engineer III
Location: Redmond, Washington
Duration: Contract
Job ID: 180519
Job Overview:
This role is an onsite contract position in Redmond, focused on bringing state-of-the-art machine learning models from research into real-time deployment on embedded devices for AR/VR and wearable technologies. As a Software Engineer III, you will work within a Reality Labs-style research environment, collaborating closely with researchers and engineers to design, train, optimize, and deploy ML models that power next-generation audio and multimodal experiences for augmented reality glasses and related devices. You will operate at the intersection of ML research, embedded systems, and high-performance C/C++ engineering, ensuring models run efficiently within strict latency, memory, compute, and power constraints.

Responsibilities

- Collaborate side by side with research scientists to transition ML models from research prototypes into efficient, real-time inference solutions on embedded devices.
- Train, fine-tune, and adapt ML models with deployment constraints in mind, including latency, memory footprint, power consumption, and compute limitations.
- Optimize models for on-device inference using techniques such as quantization, pruning, distillation, operator fusion, and architecture refinements.
- Port, integrate, and profile ML models on embedded targets such as DSPs, NPUs, microcontrollers, and mobile SoCs, identifying and resolving performance bottlenecks.
- Write clean, maintainable, and high-performance C/C++ code for on-device inference pipelines and integrate it into real-time prototype systems.
- Develop tools, scripts, and infrastructure that enable researchers to quickly evaluate model performance,



accuracy, and resource usage on target hardware.
- Work within deep learning frameworks (e.g., PyTorch or TensorFlow) to build, train, and iterate on ML models for audio, vision, and multimodal applications.
- Coordinate with internal hardware, runtime, and toolchain teams to evaluate, integrate, and leverage existing systems, SDKs, and inference runtimes.
- Contribute to the design of end-to-end ML pipelines, from data preparation and training to deployment and monitoring on embedded platforms.
- Profile and debug performance issues across both the ML model and the underlying hardware, using appropriate tools and methodologies.
- Participate in code reviews, documentation, and knowledge sharing to maintain high engineering standards and foster team-wide best practices.
- Work collaboratively as part of a multifunctional team where communication, documentation, and teamwork are highly valued.
- Continuously learn and explore new technologies, algorithms, and hardware capabilities relevant to AR/VR, audio, and embedded ML.
- Embrace ambiguity and contribute to problem solving at the frontiers of research, helping shape the future of AR/VR and wearable experiences.

Qualifications

- BS degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent work experience.




- Industry experience optimizing and running ML inference on embedded or resource-constrained devices.
- Hands-on experience training ML models using deep learning frameworks such as PyTorch or TensorFlow.
- 2+ years of industry experience writing production-quality C/C++ code.
- Experience profiling and debugging performance issues across ML models and target hardware platforms.
- Experience working in cross-functional teams, collaborating with researchers, hardware engineers, and software developers.
- Strong understanding of on-device ML as a co-design problem, balancing model architecture, quantization, and hardware capabilities.
- Ability to write clean, effective, and well-documented code suitable for real-time systems and embedded environments.
- Familiarity with embedded targets such as DSPs, NPUs, microcontrollers, and mobile SoCs, and their associated toolchains.
- Preferred: Experience with ML for audio applications (e.g., speech enhancement, source separation, keyword spotting, acoustic event detection).
- Preferred: Experience with ML for computer vision (e.g., detection, tracking, segmentation, pose estimation).
- Preferred: Knowledge of audio DSP fundamentals, including filtering, STFT/filterbanks, beamforming, and real-time audio processing.
- Preferred: Experience with on-device inference frameworks and toolchains such as ExecuTorch, TFLite/LiteRT, ONNX Runtime, or vendor NPU/DSP SDKs.
- Preferred: Experience with model compression techniques such as quantization-aware training, post-training quantization, pruning, or distillation.
- Preferred: Experience writing hardware-specific optimiz

📌 Software Engineer III (Redmond)
🏢 PTR Global
📍 Redmond