Machine Learning Engineer

Seeing Machines (SM) is the world leader in Safety-AI, developing technology that genuinely saves lives. Our state-of-the-art driver monitoring systems are used in millions of vehicles across the globe, providing real-time protection from distraction and fatigue. We work with the world’s leading OEMs across multiple transport sectors of automotive, commercial road transport (Fleet), and aviation. In automotive, we enable safer Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) solutions. In Fleet, our best-in-class aftermarket product Guardian provides safety for the drivers and fleet operators, and in aviation, our advanced gaze tracking technology understands how pilots interact and monitor instruments – leading to better training and safer operations. 

The Embedded Artificial Intelligence team is a core part of SM’s innovation engine. We are a group of engineers and researchers focused on deploying cutting-edge AI models onto the edge. We blur the line between Embedded Engineering, and Machine Learning, being deeply involved in the entire lifecycle of a machine learning models development. From analyzing state-of-the-art model architectures, designing and training models, to writing high-performance C++ inference logic for target hardware. You will be working with the absolute cutting-edge technology in the field of deep learning and computer vision; and exploring the boundaries of what is possible in the realm of driver and operator monitoring. Come, join us and help us design the cars of the future! 

We are seeking an outstanding Machine Learning Engineer who is passionate about deploying and optimizing AI systems for embedded platforms. In this role, you will be responsible for ensuring that our advanced algorithms perform flawlessly and efficiently on edge devices. 

Working closely with machine learning researchers, you will dissect model architectures, identify performance bottlenecks, and apply advanced optimization techniques to meet the strict constraints of embedded hardware. This is a hands-on role that requires deep technical expertise in both machine learning and high-performance computing, including writing production-quality C++ code to implement inference logic from the ground up. 

Your Main Responsibilities Will Include:

1. Taking ownership of machine learning models and adapting them for deployment on target edge devices (CPUs, NPUs).
2. Analyzing and optimizing model architectures and implementing cutting-edge techniques across Knowledge Distillation, Quantization, Pruning, and Neural Architecture Search (NAS).
3. Developing and maintaining high-performance C++ for model inference.
4. Collaborating with research teams to understand the science behind new algorithms and ensure their successful transition to embedded systems.
5. Continuously exploring and implementing State-of-the-Art (SOTA) practices to improve model efficiency and performance.
6. Improving our ML Ops pipelines and engineering practices using tools like Docker and MLFlow.
7. Most importantly, experimenting! 

Background, Skills, Experience & Qualifications:

Mandatory:

• Bachelor's qualification in Computer Science, Software Engineering, or an equivalent field.
• Strong programming skills and experience developing in Python. 
• Proven experience with at least one major deep-learning framework (e.g., PyTorch, TensorFlow, JAX). 
• Deep understanding of machine learning principles and deep learning architectures. 
• A proactive mindset, with an eagerness to try new approaches and solve complex problems. 
• Excellent problem-solving abilities and strong verbal and written communication skills. 
• A collaborative team player who can work effectively across multiple teams.

Desirable:

• Post-graduate qualification (PhD or Master’s) in Computer Vision, Machine Learning, or a related field.
• Hands-on experience optimizing models for performance on CPUs and/or NPUs.
• Familiarity with model optimization techniques. 
• Experience programming in C++ 
• Experience with ML Ops tools such as Docker and MLFlow. 
• Experience with embedded hardware and deploying AI systems in resource-constrained environments. 

If you are a passionate machine learning engineer who is interested in learning C++ (or who already knows!), or an embedded engineer upskilling in machine learning, and you love pushing the boundaries of what’s possible with AI on the edge, we encourage you to apply!