Job Description
This internship involves developing and deploying AI and machine learning models for satellite imagery analysis, focusing on autonomous vision systems, edge computing, and geospatial analytics to enhance satellite capabilities in real-time Earth observation.
Key Responsibilities
- Design and train computer vision models for satellite imagery, including object detection, segmentation, and change detection.
- Optimize models for edge deployment through techniques like quantization, pruning, and knowledge distillation.
- Develop algorithms for autonomous identification of high-value targets to optimize satellite tasking and data downlink.
- Profile and validate model performance on edge hardware, focusing on latency, power consumption, and memory footprint.
- Curate and augment space-ready datasets considering orbital challenges such as off-nadir angles and atmospheric noise.
- Build prototypes demonstrating autonomous systems where ML outputs influence satellite actions.
- Collaborate with flight software engineers to transition prototypes into production-ready code suitable for orbital environments.
- Analyze and improve model performance within resource-constrained hardware environments.
Requirements
- Currently pursuing or recently completed a degree in Computer Science, Robotics, Computer Engineering, Aerospace Engineering, Electrical Engineering, or a related field.
- A solid understanding of deep learning fundamentals, particularly in Computer Vision CNNs or Vision Transformers.
- Proficiency in Python programming language.
- Hands-on experience with at least one major machine learning framework such as PyTorch or JAX.
- The ability to break down complex problems and a strong desire to learn how to deploy models on resource-constrained hardware.
- Excellent communication skills with the ability to document technical workflows and explain model trade-offs such as accuracy versus speed versus power consumption.
- A collaborative mindset and the ability to work effectively within a cross-functional team of engineers.
- Experience with or knowledge of profiling and validating model performance metrics including latency, power consumption, and memory footprint on edge hardware targets like NVIDIA Jetson or similar accelerators.
- Experience in curating and augmenting space-ready datasets, accounting for orbital challenges such as varying off-nadir angles and atmospheric noise.
- Ability to build end-to-end prototypes demonstrating autonomous closed-loop systems where ML outputs directly influence satellite actions.
- Partner with flight software engineers to transition research prototypes into robust, production-ready code suitable for orbital environments.
- Analyze model performance and limitations in resource-constrained environments to propose iterative architectural improvements.
Benefits & Perks
Commuter Benefits
Paid time off for holidays and company-wide days off
Internet reimbursement
Access to LinkedIn Learning
Compensation range of $35 - $60 per hour (US) depending on experience, skills, and location
San Francisco salary range of $40 - $60 USD
Ready to Apply?
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