Blue Origin
Spaceflight
Sr.AutonomousVehicleAIEngineerComputerVisionandPathPlanning
“Sr. Autonomous Vehicle AI Engineer - Computer Vision and Path Planning at Blue Origin. Skills: AI, Machine Learning, Computer Vision, Path Planning, Autonomous Vehicles. Contribute to the development of AI-driven computer vision algorithms. Develop, train, and test machine learning models for object detection, classification, semantic segmentation, and anomaly detection specific to autonomous driving scenarios”
What You'll Achieve.
Ensure safe and efficient navigation; Optimize for accuracy, speed, and reliability; Improve the performance of perception and decision-making systems; Make a significant impact on the future of transportation; Define the safety and reliability of our autonomous vehicles
Industry & Context.
Excellent analytical and problem-solving skills; Solve some of the most challenging problems in spaceflight; Solve complex, real-world challenges
U. S. citizen or national, U. S. permanent resident (i. e. current Green Card holder), or lawfully admitted into the U. S. as a refugee or granted asylum, Blue’s Standard Background Check, Defense Biometric Identification System (DBIDS) background check if at any time the role requires one to be on a military installation, Drivers who operate Commercial Motor Vehicles may be subject to additional Federal Motor Carrier Safety Regulations, Ability to obtain and maintain Merchant Mariner Credential
What They're Looking For.
Must Have
PhD in Computer Science, Robotics, AI, Machine Learning, Aerospace Engineering, or a related field, 5 + years of experience in applying deep learning to computer vision or decision-making, Theoretical understanding of sensor fusion, environmental perception, and path planning algorithms, High proficiency in Python and/or C++, Experience using deep learning libraries such as PyTorch or TensorFlow, Ability to work collaboratively in a fast-paced, cross-functional team environment, Excellent analytical and problem-solving skills, with a creative and first-principles approach
Nice to Have
Experience designing, implementing, and training reinforcement learning (RL) agents to solve complex optimization or control problems, including problem formulation, state/action space design, and reward shaping, Experience with simulation environments (e. g. , Gazebo, NVIDIA Isaac Sim) and their application to robotics or aerospace, Familiarity with classical Guidance, Navigation, and Control (GNC) concepts, Experience with transformers and large machine learning models, Hands-on experience with deploying and debugging software on embedded systems or robotic hardware
What You'll Do.
Contribute to the development of AI-driven computer vision algorithms
and test machine learning models for object detection
semantic segmentation
and anomaly detection specific to autonomous driving scenarios
Implement and test path planning algorithms using modern decision-making techniques
and deploy neural networks for real-time performance on flight-qualified hardware
and use high-fidelity simulation environments to test and validate autonomous algorithms
Analyze data from simulations and flight tests to assess and improve the performance of perception and decision-making systems
Support the continuous improvement of in-house machine learning pipelines and tools
Optimize AI models for edge and cloud deployment
How You'll Work.
Team & Collaboration
Collaborate with a multidisciplinary team of GNC, software, and hardware engineers to integrate AI/ML solutions into the vehicle's avionics and flight software systems; Work collaboratively in a fast-paced, cross-functional team environment
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