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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI and Computer Vision Engineer - **Company:** The Exploration Company - **Location:** München, Germany - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Computer Vision, Computer Clusters, Linux, Python (Programming Language), Machine Learning, Object Detection, Raw Data, Tensorflow, Pytorch, Deep Learning, Git, Information Technology, Unreal Engine, Data Generation - **Published:** September 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7e1c7d02eda0cea0 ## About the Role * Degree (MSc or PhD) in computer science, electrical engineering, robotics, aerospace, physics, or a comparable field with a strong machine learning focus., * 3+ years building and shipping deep-learning computer vision systems such as object detection, keypoint detection, pose estimation, or 3D perception. PhD work in the field counts. * Demonstrable experience taking a model from research prototype to a constrained target: quantization, distillation, latency optimization, deployment on embedded or accelerator hardware. * Experience training on synthetic data and dealing with the sim-to-real gap. * Hands-on lab work: cameras, calibration, test setups, collecting and annotating your own data. Skills and Competencies * Strong Python and PyTorch (or JAX/TensorFlow); clean, version-controlled, reproducible code. * Solid classical computer vision and 3D geometry: camera models, intrinsics and extrinsics, distortion, PnP, RANSAC, coordinate frames. * Comfortable with Linux, Git, containers, and running large training jobs on GPU clusters or in the cloud. * Genuinely hands-on and self-directed: you will carry your work largely on your own, with review support rather than daily direction. * Able to communicate results clearly in technical reports and reviews. * Working proficiency in English; German is a plus. Nice to have * Familiarity with spacecraft rendezvous, docking, or vision-based navigation, and with benchmarks such as SPEED/SPEED+ and the ESA pose estimation challenges. * Spiking neural networks and neuromorphic hardware, or event-based cameras. * Federated learning, differential privacy, or secure aggregation. * Rendering and synthetic data generation (Blender, Unreal Engine, Isaac Sim, …). * Experience with space or safety-critical software assurance, or with publicly funded R&D projects. ## Description In your capacity as AI and Computer Vision Engineer, your role will be continuously evolving, but day to day your duties will include: * Designing, training and evaluating deep-learning models for 6-DoF pose estimation of non-cooperative spacecraft * Owning the full training pipeline: dataset generation and management, augmentation, domain adaptation between synthetic, laboratory and orbital imagery, experiment tracking and reproducibility. * Optimizing models for flight-representative compute (knowledge distillation, pruning, quantization and quantization-aware training) and benchmarking latency, memory and power against onboard constraints. * Porting and evaluating models on embedded and neuromorphic hardware, and characterizing the accuracy versus energy trade-off. * Building explainability and uncertainty into the pipeline so failure modes such as high occlusion can be debugged and the technology is a credible candidate for certification. * Exploring privacy-preserving and distributed training approaches that let us improve models with partners without exchanging raw data. * Prototyping lightweight self-supervised refinement methods for later in-flight model adaptation on unlabeled imagery. * Defining requirements, test scenarios and validation criteria together with GNC/FPO, and supporting the selection and characterization of space-qualified camera sensors. * Running validation campaigns on hardware-in-the-loop testbeds and analyzing the results. * Managing our training compute footprint across cloud GPU and internal HPC efficiently. ## Related Videos - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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