Job Description
Job Role: Software Engineer III
Location: Redmond, WA (Onsite)
Duration: 12 Months
BS degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent work experience
We are seeking a contract ML Software Engineer to bring state-of-the-art machine learning out of the lab and onto the device. Research models only matter to our prototypes if they run in real time, within tight compute, memory, and power budgets, on the embedded hardware that powers wearables. This role sits at that boundary: you will train and adapt models alongside research scientists, then own the work of optimizing, porting, and running them efficiently on embedded targets.
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
Location: Redmond, WA (Onsite)
Duration: 12 Months
BS degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent work experience
We are seeking a contract ML Software Engineer to bring state-of-the-art machine learning out of the lab and onto the device. Research models only matter to our prototypes if they run in real time, within tight compute, memory, and power budgets, on the embedded hardware that powers wearables. This role sits at that boundary: you will train and adapt models alongside research scientists, then own the work of optimizing, porting, and running them efficiently on embedded targets.
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 Reality Labs 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
Minimum Qualifications
- 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
Numbers & Facts
| Location | Redmond, OR |
| Industry | Other/Not Classified |
| Salary | $80–$90 Per Hour |
| Company Size | 100 to 499 employees |
| Year Founded | 1998 |
| Website | www.eteaminc.com |
About Company
Looking for a great job? Join eTeam. We’re looking for talented staffing professionals to join our staff. We also provide contract assignments and full-time jobs at Fortune 2000 Companies. We’ve been named one of the best companies to work for by Staffing Industry Analysts and New Jersey Business.
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