Contribute to the development of AI-driven computer vision algorithms to accurately perceive and understand a vehicle's environment using data from cameras, LiDAR, and other sensors.
Develop, train, and test machine learning models for object detection, classification, semantic segmentation, and anomaly detection specific to autonomous driving scenarios.
Implement and test path planning algorithms using modern decision-making techniques to ensure safe and efficient navigation.
Train, validate, and deploy neural networks for real-time performance on flight-qualified hardware, optimizing for accuracy, speed, and reliability.
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.
Design, build, and use high-fidelity simulation environments to test and validate autonomous algorithms against a wide range of mission scenarios and off-nominal conditions.
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 for data management, model training, and performance monitoring.
Optimize AI models for edge and cloud deployment, addressing challenges such as latency, model compression, and distributed computing.
Requirements
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.
Strong theoretical understanding of sensor fusion, environmental perception, and path planning algorithms.
High proficiency in Python and/or C++, along with experience using deep learning libraries such as PyTorch or TensorFlow.
A passion for space exploration and a desire to apply your skills to solve complex, real-world challenges.
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.
Desired:
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.
Compensation Range for:
WA applicants is $197,529.00 - $276,539.55
Other site ranges may differ
Culture Statement
Export Co
Benefits
Vision insurance
Additional Information
Application close date:
Applications will be accepted on an ongoing basis until the requisition is closed.
At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We're working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!
This role is part of Advanced Concepts and Enterprise Engineering (ACE), supporting Blue Origin's mission of millions of people living and working in space for the benefit of Earth. The team fosters innovation and drives engineering workflows of the future, shared solutions and standards, simplicity and lower costs, and manufacturable design.
This position is for a passionate and driven engineer who wants to apply their skills in AI, machine learning, and robotics to solve some of the most challenging problems in spaceflight. You will be part of a dynamic team responsible for creating systems that allow our vehicles to perceive their environment, make intelligent decisions, and execute complex maneuvers with precision and safety.
Blue Origin · Greater Seattle Area