Job Title: Software Engineer III
Schedule: Hybrid
Location: Redmond, Washington
Employment Type: 1 year contract (possibility for extension)
Compensation: $90-95 per hour on a W2
Job Summary
The Reality Labs Research (RLR) team is developing technologies that will help shape the future of virtual and augmented reality, including next-generation AR glasses and wearable devices. The RLR Audio team focuses on advancing audio technologies that improve human communication and connection, bringing research innovations from early development through integration into real-world products.
We are seeking a Contract ML Software Engineer to help bring state-of-the-art machine learning models out of the research environment and onto embedded devices. This role will work closely with research scientists to train and adapt ML models and then optimize, port, and deploy them for real-time performance within strict compute, memory, latency, and power constraints.
The ideal candidate is comfortable working with both PyTorch/TensorFlow model development and C/C++ inference environments. This person should understand on-device ML optimization and be capable of balancing model architecture, quantization, hardware limitations, accuracy, and performance. Experience with audio ML, computer vision ML, audio DSP, AR/VR, or wearable technologies is highly desirable.
Key Responsibilities
- Partner with research scientists to transition ML models from research prototypes to efficient, real-time inference on embedded devices.
- Train, fine-tune, and adapt machine learning models while accounting for latency, compute, memory, and power constraints.
- 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 hardware, including DSPs, NPUs, microcontrollers, and mobile SoCs.
- Identify and resolve performance bottlenecks across ML models and target hardware.
- Develop clean, maintainable, high-performance C/C++ code for real-time inference pipelines.
- Build tools and infrastructure that enable researchers to evaluate model accuracy and performance on target hardware.
- Collaborate with internal hardware, runtime, and toolchain teams to evaluate and integrate existing systems.
- Work effectively within a cross-functional research and engineering environment.
- Document work clearly and communicate technical findings with team members.
- Learn new technologies and operate effectively within the ambiguity of cutting-edge AR/VR research.
Minimum Qualifications
- BS degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent professional experience.
- Industry experience optimizing and running machine learning inference on embedded or resource-constrained devices.
- Experience training ML models using PyTorch, TensorFlow, or similar deep learning frameworks.
- 2+ years of industry experience with C/C++.
- Experience profiling and debugging performance issues across ML models and target hardware.
- Experience collaborating within cross-functional technical teams.
Preferred Qualifications
- Experience with audio machine learning, including speech enhancement, source separation, keyword spotting, or acoustic event detection.
- Experience with computer vision ML, including detection, tracking, segmentation, or pose estimation.
- Knowledge of audio DSP, including filtering, STFT/filterbanks, beamforming, or real-time audio processing.
- Experience with on-device inference frameworks and toolchains such as ExecuTorch, TFLite/LiteRT, ONNX Runtime, or vendor NPU/DSP SDKs.
- Experience with model compression and optimization, including quantization-aware training, post-training quantization, pruning, or distillation.
- Experience developing hardware-specific optimizations using SIMD/NEON, DSP intrinsics, or fixed-point arithmetic.
- Experience within AR/VR or wearable device development.
- Ability to gather technical requirements and work closely with researchers to develop novel solutions.
- Experience writing code and developing tools that support research initiatives.
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