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Senior AI and Computer Vision Engineer (Spacecraft Pose Estimation & Autonomous Systems)
Visa Sponsorship
Relocation
AI Summary
Build and deploy deep-learning models for 6-DoF pose estimation of non-cooperative spacecraft using synthetic/lab data, optimizing for embedded and neuromorphic hardware. Own the full pipeline—from dataset generation to model deployment—while ensuring robustness, explainability, and certification-readiness in a fast-paced aerospace R&D environment.
Key Highlights
Develop and optimize deep-learning models for autonomous spacecraft pose estimation in constrained flight hardware.
End-to-end ownership of training pipelines, including synthetic-to-real domain adaptation and hardware deployment.
Collaborate with GNC/FPO teams to define validation criteria and hardware-in-the-loop testing for space-qualified systems.
Key Responsibilities
Design, train, and evaluate deep-learning models for 6-DoF pose estimation of non-cooperative spacecraft.
Manage synthetic/lab-to-real domain adaptation, augmentation, and experiment reproducibility pipelines.
Optimize models for flight hardware (quantization, distillation, pruning) and benchmark latency/memory/energy trade-offs.
Port and evaluate models on embedded/neuromorphic hardware, ensuring accuracy-energy balance.
Integrate explainability and uncertainty quantification for certification and failure-mode debugging.
Prototype self-supervised refinement methods for in-flight model adaptation on unlabeled imagery.
Define validation requirements, test scenarios, and hardware-in-the-loop testbed campaigns.
Collaborate with GNC/FPO teams to select and characterize space-qualified camera sensors.
Efficiently manage distributed training compute across cloud GPU and internal HPC resources.
Technical Skills Required
Benefits & Perks
Relocation assistance provided
Visa sponsorship where applicable
Equal opportunity employer with diversity focus
Nice to Have
Experience with spacecraft rendezvous/docking or vision-based navigation
Familiarity with neuromorphic hardware (spiking neural networks, event-based cameras)
Knowledge of federated learning, differential privacy, or secure aggregation
Rendering/synthetic data generation (Blender, Unreal Engine, Isaac Sim)
Space or safety-critical software assurance experience
Job Description
Here at The Exploration Company, we are building innovative aerospace technologies that advance the future of space transportation.
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.
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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.
- 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.
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- 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.
- 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.
Interested in relocating to Germany? Check out our comprehensive Relocation Jobs in Germany page with detailed relocation packages and benefits.
What makes us special here at The Exploration Company and why we think you will enjoy working here is:
- We’re Agile - we make decisions fast whilst keeping our goals and systems in mind
- We’re Open and Collaborative - we are transparent about risks and obstacles, so that we can cooperate to overcome them
- We have a lot of Fun - we refuel our energy knowing we are democratising space. It’s a wonderful and rare opportunity, are YOU up for the challenge?
The Exploration Company is an equal opportunity employer and values diversity. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status. Relocation assistance is provided for those willing to relocate including visa sponsorship where applicable.
All offers are contingent upon the successful completion of a background check, which will be subject to the country, role, and project you applied for. By submitting your application, you acknowledge and agree that any offer of employment may be conditional upon successful condition of this check. The Exploration Company partners with Zinc Work Limited located in the UK (Zinc), a third-party provider, to ensure this process as efficient and transparent as possible.
If applicable, you will receive an email from Zinc upon acceptance of your offer with a link to a secure portal where you can provide the required information to Zinc.
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