Machine Learning Engineer - Computer Vision & Deep Learning

About the Company

A fast-growing technology firm specializing in advanced AI and computer vision solutions is expanding its machine learning capabilities globally. The company boasts a successful international product with strong customer adoption and an established engineering team.

About the Role

This position demands a hands-on Machine Learning Engineer capable of designing, training, enhancing, and deploying models into production environments—not merely integrating existing AI APIs.

Key Responsibilities

• Collaborate closely with product teams, software engineers, and ML specialists to develop novel machine learning functionalities from scratch.
• Focus primarily on computer vision and deep learning by handling extensive real-world visual datasets to create dependable production ML systems.
• Engage comprehensively in the ML lifecycle, including custom model design, development using Python and PyTorch, building training pipelines, data preparation and labelling, and conducting experiments to assess model effectiveness.
• Optimize models in terms of accuracy, latency, and inference speed, and manage their transition from research to production deployment.
• Develop supplementary software around the ML lifecycle such as model versioning, deployment, monitoring, and support both cloud and edge inference platforms.
• Investigate and potentially incorporate large language models (LLMs), multimodal architectures, and generative AI where they genuinely enhance the product.

Qualifications and Experience

• Approximately 4 to 8 years of professional experience in machine learning engineering, with flexibility for exceptional candidates beyond this range.
• Demonstrated strong expertise in computer vision with a track record of building and improving image-based ML models.
• In-depth knowledge of modern deep learning techniques beyond traditional machine learning.
• Proficiency in PyTorch or similar frameworks with hands-on experience in developing and training machine learning models from the ground up.
• Strong software engineering skills to write production-quality code and an understanding of complexities involved in deploying ML models in real-world products.
• Experience with the full spectrum of production ML concerns such as training pipelines, model evaluation, version control, deployment, monitoring, data pipelines, inference, and optimization.
• Background in domains like robotics, autonomous systems, industrial AI, smart cameras, medical imaging, satellite imagery, defence technology, IoT, physical AI, image recognition, video analytics, edge AI, or multimodal AI is advantageous.
• Experience deploying ML models to edge devices or other resource-constrained environments is especially valued.

What This Role Is Not

• It is not a typical data science position focused on analytics, dashboards, forecasting, or statistical modelling.
• It is not exclusively an MLOps role nor a purely academic research job.
• It is not primarily an AI Engineer role centered on integrating third-party foundation models or building retrieval-augmented generation (RAG) systems.

Preferred Attributes

• Experience in startup or scale-up environments where ownership of comprehensive problems is typical.
• Comfort with ambiguity and a principled approach to problem solving.
• Ability to transition fluidly between experimentation and engineering phases.
• Strong curiosity to understand why models behave as they do.

Opportunity and Compensation

• Join a profitable, rapidly expanding international tech company with institutional backing and strong client retention.
• Influence the architecture, tools, and strategic direction of a small, focused ML team.
• Opportunity to engage progressively with advanced computer vision, edge AI, multimodal, and generative AI challenges.
• Competitive total compensation package including salary up to A$180,000 plus superannuation and equity options.