BlueSpace.ai
Machine Learning Engineer: Perception and Planning
Hybrid
Full Time
#Engineering
#Autonomous
#Machine Learning
#Deep Learning
#Python
#C++
#PyTorch
#TensorFlow
#Linear Algebra
#Probability
#Software Engineering
#Data Processing
BlueSpace.ai is looking for a Machine Learning Engineer: Perception and Planning
Full TimeSeniorHybridUnited StatesEngineeringAutonomousMachine LearningDeep LearningPythonC++PyTorchTensorFlowLinear AlgebraProbabilitySoftware EngineeringData ProcessingEnglishHybrid work
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.
Share this job
Copy Permalink
