Embedded Machine Learning Software Engineer

Location:

Fully Onsite at Redmond, WA

Contract Duration:

12 Months

Pay Rate:

$90/hour on W2

Benefits

Health

Dental

Visio

401K

PTO

Note: This is a W-2-only position. We do not offer visa sponsorship and cannot accept H-1B, F-1/STEM OPT, 1099, C2C, or C2H arrangements.

Our client's team brings together world-class researchers, developers, and engineers to develop technologies that will shape the future of augmented and virtual reality. The team works across optics, displays, computer vision, audio, graphics, haptics, and advanced perception to enable breakthrough AR glasses, immersive experiences, and next-generation wearable devices.

The Audio Research team focuses on advancing human hearing and auditory machine intelligence to improve communication and connection. The team develops and matures audio technologies for AR glasses and wearables, translating early-stage research into innovative capabilities for future products.

Job Overview We are seeking a

Embedded

Machine Learning Software Engineer

to bridge the gap between machine learning research and real-world, on-device deployment. In this role, you will collaborate closely with research scientists to transform advanced ML models into efficient, real-time solutions that run on embedded hardware under strict compute, memory, latency, and power constraints.

The immediate focus is optimizing and deploying speech extraction models for on-device inference, with opportunities to contribute to a broader range of audio and multimodal machine learning initiatives.

The ideal candidate combines hands-on experience training and adapting models using





PyTorch or similar deep learning frameworks

with strong

C/C++ software engineering

skills and practical experience optimizing ML inference on embedded or resource-constrained devices.

You will take ownership of the deployment process, from model adaptation and optimization through hardware integration, profiling, and performance tuning. This is an prospect to work alongside leading researchers, solve challenging on-device ML problems, and help bring next-generation AR/VR and wearable technologies to life.

Key Responsibilities

Partner with research scientists to transition machine learning models from research prototypes to efficient, real-time inference on embedded devices.

Train, fine-tune, and adapt ML models while accounting for deployment requirements, including latency, memory consumption, compute capacity, and power efficiency.

Optimize models for on-device execution using techniques such as quantization, pruning, knowledge distillation, operator fusion, and architecture adjustments.

Port, integrate, profile, and debug ML models on embedded platforms, including DSPs, NPUs, microcontrollers, and mobile system-on-chips (SoCs).

Identify and resolve performance bottlenecks across model architectures, inference runtimes, and target hardware.

Develop clean, maintainable,



high-performance

C/C++ code

for on-device inference pipelines and real-time prototype systems.

Build tools and infrastructure that accelerate model evaluation and enable researchers to assess model accuracy and performance on target hardware.

Collaborate with hardware, runtime, and toolchain teams to evaluate and integrate existing technologies and deployment solutions.

Document findings, share technical expertise, and contribute to engineering discussions within a multidisciplinary team.

Navigate evolving research requirements, explore unfamiliar technologies, and develop practical solutions to complex AR/VR engineering challenges.

Must Have Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.

Industry experience optimizing and

deploying machine learning inference

on

embedded or resource-constrained devices .

Hands-on experience training machine learning models using

PyTorch, TensorFlow, or similar deep learning frameworks .

2+ years of industry experience developing software in C/C++.

Experience profiling, debugging, and optimizing performance across ML models and target hardware.

Experience collaborating with cross-functional engineering or research teams.

Preferred Qualifications

Experience developing

audio machine learning

solutions, such as speech enhancement, speech extraction, source separation, keyword spotting, or acoustic event detection.

Experience with

computer vision machine learning , including obje

📌 Machine Learning Engineer (Redmond)
🏢 Tailored Management
📍 Redmond