As an ML Engineer, you will play a key role in taking cutting-edge research and turning it into robust, real-world driving intelligence deployed at scale for a prestigious autonomously operated vehicles and AI organisation.
You’ll work at the intersection of research, data, and infrastructure—collaborating closely with scientists and platform engineers to integrate, validate, and productionize large-scale neural networks that power autonomous vehicles.
What You’ll Do
You’ll work at the intersection of research, data, and infrastructure—collaborating closely with scientists and platform engineers to integrate, validate, and productionize large-scale neural networks that power autonomous vehicles.
What You’ll Do
- Translate state-of-the-art machine learning research into production-ready systems
- Productionize and scale end-to-end deep learning models for real-world driving scenarios
- Collaborate across research, data, validation, and platform teams to deliver reliable ML features
- Design and implement training, validation, and testing pipelines with strong performance metrics
- Optimize systems for large-scale data processing and high-performance execution
- Experience shipping ML models or features in production environments
- Strong understanding of deep learning workflows, including training, evaluation, and metrics
- Solid grasp of modern machine learning literature and applied research
- Proficiency in writing clean, efficient, and well-tested Python code
- Experience with distributed, parallel, or high-performance computing systems
- Ability to work effectively with large-scale datasets and build visualization tools
- Strong understanding of core computing principles (performance, scalability, reliability, security)
- Driven to bridge the gap between research and real-world deployment
- Comfortable operating in a fast-moving, collaborative environment
- Curious, hands-on, and motivated to solve complex real-world problems at scale
