Company Description:
The company is based in Barcelona and aims to revolutionize remote sensing for environmental monitoring and resource management.
Aistech Space is seeking a highly specialized Machine Learning and Embedded AI Systems Engineer to serve as the critical bridge between data science, software, and hardware development. This role is focused on the successful integration and performance optimization of ML models across diverse and often constrained operational environments, including in-orbit systems (satellites, embedded TPUs), on-ground processing, and online platforms. The successful candidate will drive the deployment lifecycle, ensuring our AI systems are reliable and performant across the entire Aistech Space ecosystem.
Embedded Deployment: Collaborate with FPGA hardware, embedded software, and data science teams to deploy AI solutions directly onto satellites and other constrained Edge AI devices.
Implement high-performance solutions by transferring and optimizing algorithms initially created in Python into robust C/C++ codebases.
Research, recommend, and implement new hardware and software solutions to improve the company’s overall AI infrastructure.
Performance Optimization: Provide computational and deployment support to the Remote Sensing and Data Science teams.
Masters/PhD in Computer Science, Engineering, or a related technical field.
Fluency in English.
More than 2 years of professional experience in Embedded Software Development.
Programming fluency in C, C++, and Python.
Proficiency with Linux environments and collaborative development using GitHub.
Expertise in ML/Deep Learning deployment frameworks, such as TensorFlow Lite, ONNX Runtime, or PyTorch Edge.
Working knowledge of MLOps principles for training/evaluation pipelines and automated model delivery/monitoring.
Experience with AMD Versal AI engines and Vitis Model Composer (Kernel development, data flow optimization, model quantization/pruning, Vitis IDE, and performance analysis are a plus).
Experience deploying models via web services, dashboards, and APIs (e.g., FastAPI, Flask, gRPC) and using cloud services/containerization (GCP, AWS, Azure, Docker, Kubernetes).
Familiarity with High-Performance Computing (HPC) and job scheduling systems like SLURM.
Knowledge of data compression techniques for in-orbit data handling.
Prior experience in the aerospace or remote sensing industries.