Senior ML Engineer (f/m/x) - Computer Vision for Earth Observation

We are looking for a Senior ML Engineer (f/m/x) to build and scale computer vision systems for Earth observation. The core of the role is geometric computer vision on very high resolution satellite imagery: 3D reconstruction from stereo and multi-view data, and robust image matching and registration across sensors, viewpoints, and time. These systems turn raw optical imagery into accurate 3D surface models and precisely aligned image stacks that downstream products can rely on. Around that core, the role extends into broader CV problems such as segmentation, detection, and change analysis, and into making models run reliably in production, including under constrained compute where projects require it.

A central focus of your first project will be the generalization of stereo reconstruction methods: making learned stereo perform reliably across sensors, geographies, and acquisition conditions. We have already done substantial modeling work here, and experience with synthetic data generation and sim2real transfer would be especially valuable in taking it further.

This is a balanced role: part applied research, part engineering, all impact. The exact balance depends on your strengths, and we are open to profiles that lean more toward applied research or more toward engineering as long as the fundamentals are strong.

Tech stack and tools, which potential candidate will work with:

• Core ML: Python, PyTorch + PyTorch Lightning

• Experimentation: Databricks + MLflow (tracking, model registry)

• Compute & orchestration: Ray (distributed compute), Prefect (workflows)

• Infrastructure: AWS and secure on-prem environments

• Geospatial: GDAL, Rasterio, GeoPandas, STAC

• Datastores: PostgreSQL (metadata / operational data)

As a Senior ML Engineer, you will drive the development of state-of-the-art computer vision systems that reconstruct 3D structure from, and robustly align, large volumes of satellite imagery.

• Drive geometric CV development: design, train, and iterate on stereo/multi-view 3D reconstruction models and image matching/registration pipelines for VHR optical imagery (co-registration, alignment, robust correspondence), with clear ablations and measurable performance improvements.

• Research to production: identify and adapt state-of-the-art approaches in 3D reconstruction, depth estimation, feature matching, and adjacent geometric CV (papers → prototypes → validated baselines), focusing on pragmatic wins under real constraints.

• Tackle generalization head-on: close domain gaps in learned stereo across sensors, geographies, and acquisition conditions, including through synthetic data and sim2real transfer strategies.

• Broader CV where projects need it: contribute to semantic tasks such as segmentation, detection, and change analysis that build on the aligned imagery and 3D reconstructions the core work produces.

• Own EO data quality: standardization and preprocessing for high-resolution imagery (normalization/calibration, tiling, pairing and co-registration sanity checks, sampling/augmentation), plus dataset-quality diagnostics.

• Build scalable pipelines: training and evaluation infrastructure across cloud and secure on-prem environments, with experiment tracking, reproducibility, and systematic failure analysis across geographies and acquisition conditions.

• Deliver production-ready components: robust inference interfaces, model packaging, deterministic evaluation, and monitoring, plus, where relevant, adaptation of models to constrained or on-device compute.

• Collaborate and communicate: work with the data annotation function on labeling guidelines and edge cases, with partner teams to turn model capabilities into validated deliverables, and with external researchers, presenting findings clearly and efficiently.

• Strong computer vision fundamentals (representation learning, supervision strategies, evaluation design) and practical debugging/optimization skills.

• Practical experience in at least one area of geometric computer vision: stereo/multi-view reconstruction, depth estimation, or image matching/registration. 

• Strong Python engineering fundamentals with clean, maintainable code, and deep experience with PyTorch, implementing and training deep learning models at scale.

• Strong understanding of ML experimentation, versioning, and tracking.

• Background in remote sensing, computer science, physics, or a related field, or equivalent practical experience. A PhD in one of these fields is a plus.

• Comfortable working with researchers and presenting findings clearly and efficiently.

• Eligibility to obtain a German security clearance (Sicherheitsüberprüfung).

• You take ownership and proactively push work forward.

• You communicate clearly and collaborate smoothly within and across teams.

• Pragmatic mindset: you balance deep research with practical delivery.

• You enjoy working with complexity and turning ambiguity into structure.

• Hands-on experience with satellite / remote-sensing imagery is a plus.

• Experience with synthetic data generation and sim2real / domain adaptation for geometric vision tasks is a plus.

• Broader geometric CV: structure-from-motion, SLAM / visual odometry, or neural 3D representations (e.g. NeRF, Gaussian splatting) is a plus. is a plus.

• 3D / photogrammetry tooling: NASA Ames Stereo Pipeline, MicMac, COLMAP; DSM generation is a plus.

• Experience deploying models under constrained compute or on edge devices (model compression, quantization, optimization) is a plus.

• Distributed computing with Ray; workflow orchestration with Prefect (or similar) is a plus.

• Cloud platforms (AWS) and/or secure on-prem / HPC experience (SLURM, Docker, DVC) is a plus.

• Experience with GDAL, Rasterio, GeoPandas, STAC is a plus.

• Experience with PostgreSQL (or similar) is a plus.

• Experience with SAR alongside optical imagery, or familiarity with geospatial foundation models/ VLMs (self-supervised, contrastive, masked modeling) is a plus.

• The opportunity to create a product that can improve business processes and lives across the globe.

• Flexible working hours and hybrid work model - we trust our employees to get their work done while maintaining a healthy work-life balance.

• We empower employees to drive their own career development, take initiative and have the freedom to be creative and bold.

• Not an overtime culture - we take care that overtime is done only as a necessity and always offset with time off and rest.

• A collaborative and learning environment - frequent internal workshops, knowledge sharing sessions, journal clubs and hackathons.

• Office located in the centre of Berlin Kreuzberg with free fruit, nuts and drinks.

• Potential to participate in the employee stock option program.

• Urban Sports membership and BVG subsidy, corporate pension program.

• A diverse and vibrant international environment of 30+ different nationalities