AI and Computer Vision Engineer

We want you as a hands-on AI and Computer Vision Engineer to build the perception capability behind autonomous close-proximity operations: models that estimate the relative position and orientation of non-cooperative spacecraft from camera images, trained largely on synthetic and lab data, and optimized to run on the compute we can actually fly.

This is a builder role. You write the training code, run the experiments, take the models onto embedded and neuromorphic hardware, and own the results.

Key Responsibilities

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.

In this role, ideally, you will have the following:

Education

• Degree (MSc or PhD) in computer science, electrical engineering, robotics, aerospace, physics, or a comparable field with a strong machine learning focus.

Experience

• 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.

• We’re Agile - we make decisions fast whilst keeping our goals and systems in mind