Machine Learning Engineer: Perception and Planning at BlueSpace.ai

BlueSpace.ai is transforming the autonomous vehicle industry through our patented 4D Predictive Perception technology, which moves beyond traditional data-reliant methods to set new benchmarks for safety and efficiency. Our team of experts from leading research institutions and global AV companies is looking for a senior-level Machine Learning Engineer to help us define the future of mobility from our offices in the United States.

Responsibilities

• Design and implement state-of-the-art deep learning models for applications like object detection, segmentation, and behavior prediction.
• Research and deploy advanced machine learning algorithms for both on-vehicle systems and datacenter environments.
• Build and manage data processing pipelines and identify necessary datasets for model training.
• Ensure high software quality through rigorous code reviews, continuous integration, and automated testing.
• Troubleshoot and resolve technical challenges that arise during real-world testing and customer deployments.

Must-haves

• A Bachelor’s degree in Computer Science, Mechanical Engineering, Electrical Engineering, or a related field.
• Proven experience in machine learning engineering, specifically with computer vision or other complex model architectures.
• Practical experience training, fine-tuning, and deploying deep learning models in production environments.
• Strong proficiency in Python or C++.
• Solid analytical foundation in linear algebra, geometry, and probability.
• Hands-on experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.

Nice-to-haves

• Professional background working with low-latency systems, such as robotics or autonomous vehicle projects.
• Experience developing models using sensor data from lidar, radar, or cameras.
• Familiarity with multi-modal machine learning models.
• A history of academic excellence, including top-tier research publications.
• Expertise in model optimization for production, including tools like ONNX or TensorRT, or experience deploying to specialized hardware like Jetson.
• Leadership potential to guide the machine learning team as we scale.

Benefits

• Hybrid work environment.