Software Engineer III - Embedded Machine Learning

Intelliswift - An LTTS Company; Redmond; On-site; AUD 158,000 - 244,000; Full time

Job summary

Intelliswift - An LTTS Company is seeking an Embedded Machine Learning Engineer to deploy ML models on wearable, audio, and intelligent devices. The role emphasizes transitioning research models to production on resource-constrained hardware with a focus on latency, memory, and power efficiency.

The candidate will train and fine-tune models using PyTorch or TensorFlow and develop high-performance C/C++ software for on-device inference, working closely with researchers and engineers in a

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
• Experience deploying and optimizing ML models on embedded or resource-constrained devices.
• Hands-on experience with PyTorch or TensorFlow.

Responsibilities

• Collaborate with researchers and engineers to transition ML models from prototype to deployment.
• Train, fine-tune, and adapt deep learning models with deployment constraints in mind.
• Optimize models for on-device inference via quantization, pruning, distillation, and architecture optimization.
• Deploy, integrate, and profile ML models on embedded hardware platforms.
• Develop high-performance C/C++ software for inference pipelines and real-time systems.
• Analyze and resolve performance bottlenecks related to latency, memory, and power.
• Build tools and infrastructure that accelerate model evaluation and deployment workflows.
• Partner with cross-functional teams to integrate ML into embedded systems.
• Document technical solutions and contribute to best practices across engineering.
• Stay current with advancements in ML, embedded computing, and edge AI.

Skills

C/C++ development

Embedded hardware

Model deployment

PyTorch / TensorFlow

Education

Bachelor's degree in CS/CE/EE

Tools

PyTorch

TensorFlow

Job description

Job Title: Software Engineer III - Embedded Machine Learning

Location: Redmond, WA (Onsite)

Duration: 12-Month Contract

We are seeking an experienced Embedded Machine Learning Engineer to help bring cutting-edge AI and machine learning models from research environments to real-world devices. This role sits at the intersection of machine learning, embedded systems, and software engineering.

The ideal candidate is equally comfortable training and fine-tuning models in modern deep learning frameworks and optimizing them for efficient, real-time execution on resource-constrained hardware. You will collaborate with research and engineering teams to deploy advanced ML solutions while balancing latency, memory, power, and compute requirements.

This is an exciting opportunity to work on next-generation wearable, audio, and intelligent device technologies while helping transform innovative research into impactful products.

Responsibilities

• Collaborate with researchers and engineers to transition machine learning models from prototype to deployment.
• Train, fine-tune, and adapt deep learning models with real-world deployment constraints in mind.
• Optimize models for on-device inference through techniques such as quantization, pruning, distillation, and architecture optimization.
• Deploy, integrate, and profile ML models on embedded hardware platforms.
• Develop high-performance C/C++ software for inference pipelines and real-time systems.
• Analyze and resolve performance bottlenecks related to latency, memory consumption, and power efficiency.
• Build tools and infrastructure that accelerate model evaluation and deployment workflows.
• Partner with cross-functional teams to integrate machine learning capabilities into embedded systems.
• Document technical solutions and contribute to best practices across the engineering organization.
• Stay current with advancements in machine learning, embedded computing, and edge AI technologies.

Required Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
• Experience deploying and optimizing machine learning models on embedded or resource-constrained devices.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Minimum 2 years of professional C/C++ development experience.
• Experience profiling and debugging performance issues across software and hardware environments.
• Ability to work effectively within cross-functional engineering teams.

Preferred Qualifications

• Experience with audio-related machine learning applications such as speech enhancement, source separation, keyword spotting, or acoustic event detection.
• Experience with computer vision applications including detection, tracking, segmentation, or pose estimation.
• Understanding of audio signal processing concepts and real-time processing systems.
• Experience with model compression and optimization techniques.
• Familiarity with embedded AI deployment frameworks and edge inference runtimes.
• Experience optimizing software for specialized hardware accelerators or low-power devices.
• Background in wearable devices, AR/VR technologies, or edge computing environments.
• Strong problem-solving skills and ability to work in research-driven, fast-paced environments.