Machine Learning Engineer

About Us

At Neuracore, we're building the world's first robot learning cloud service (https://github.com/NeuracoreAI/neuracore).

Our platform eliminates the complexity of traditional robotics development by providing a complete end-to-end solution for data collection, model training, and deployment that works across different robot types and configurations.

Our multidisciplinary team is at the forefront of making robot learning accessible to organisations worldwide, from manufacturing and logistics to healthcare and research institutions. We're transforming how robotics teams develop, train, and deploy intelligent systems by providing cloud-native infrastructure that scales from small research projects to enterprise-wide robot fleets.

About the Role

We are seeking a Machine Learning Engineer to implement and integrate cutting-edge AI systems for robotics applications. You'll research and implement advanced learning algorithms, build scalable data pipelines, and translate breakthrough research into production systems. This role offers the opportunity to work on foundation models for robotics, multi-embodiment learning, and the infrastructure that will accelerate robotics development across industries.

Key Responsibilities

• Translate research to production by implementing state-of-the-art algorithms from top-tier conferences into deployed systems
• Train deep learning models that handle multi-modal sensor data from different robot configurations and manufacturers
• Collaborate with senior scientists to refine existing algorithms, suggesting improvements to maximise success rate on target applications
• Utilise existing training infrastructure leveraging multi-GPU systems and distributed computing for large-scale model training
• Create experimentation frameworks for large-scale evaluation and testing of robot learning approaches

Required Skills

• Advanced degree (PhD, Master's, or equivalent experience) in Computer Science, Robotics, Machine Learning, or related field
• Deep ML expertise in PyTorch or TensorFlow with experience in distributed training and optimisation
• Robotics learning experience with practical knowledge of reinforcement learning and/or imitation learning
• Production software skills with ability to build scalable, maintainable systems beyond research prototypes
• Cloud infrastructure experience with platforms like AWS, GCP, or Azure and containerisation technologies

Preferred Skills

• Multi-robot systems experience and cross-embodiment learning approaches
• MLOps expertise including experiment tracking, model versioning, and deployment pipelines
• Computer vision and sensor fusion for robotics applications
• Real-world deployment experience with robot data collection and production model serving
• Robotics middleware experience with ROS/ROS2 or similar frameworks
• Familiarity with simulation environments and synthetic data generation