About the role

Senior ML Engineer developing and scaling computer vision models for satellite analytics. Involved in the full ML R&D lifecycle in a dynamic startup environment.

Responsibilities

Identify and adapt state-of-the-art approaches in remote sensing and foundation models (papers → prototypes → validated baselines), focusing on pragmatic success under real-world constraints.
Design, train, and iterate on bitemporal and multimodal SAR–optical models (alignment/fusion, robust embeddings, bitemporal/multitemporal representations) with clear ablations and measurable performance improvements.
Own EO data standardization and preprocessing for high-resolution SAR and optical imagery (normalization/calibration decisions, tiling/chipping, pairing/co-registration sanity checks, sampling/augmentations) and drive dataset-quality diagnostics.
Build scalable training and evaluation pipelines in our stack (Databricks, PyTorch Lightning, MLflow), including experiment tracking, reproducibility, and systematic debugging.
Deliver production-ready ML components (robust inference interfaces, model packaging, deterministic evaluation, monitoring signals/model cards) that downstream teams can rely on.
Work closely with product teams to ensure models translate into business value, and with the data annotation team to define labeling guidelines and close feedback loops on edge cases and quality.

Strong Python engineering fundamentals with clean, maintainable code style.
Deep experience with PyTorch and PyTorch Lightning.
Experience implementing and training deep-learning models at scale.
Strong knowledge of ML experimentation, versioning, and tracking using MLflow and Databricks.
Solid foundation in computer vision fundamentals (representation learning, supervision strategies, evaluation design) and practical debugging/optimization skills.
Hands-on experience with satellite imagery — strong preference for experience with optical and SAR data.
Proven ownership mentality and ability to drive work proactively.
Clear communication skills and aptitude for collaborating within and across teams.
Pragmatic mindset: balancing deep research with practical implementation.
Enjoy working with complexity and turning ambiguity into structured solutions.

Flexible working hours and a 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.
No overtime culture — overtime is only expected when necessary and is always compensated with time off and rest.
A collaborative, learning-oriented environment — frequent internal workshops, knowledge sharing, journal clubs, and hackathons.
Office in central Berlin (Kreuzberg) with free fruit, nuts, and beverages.
Opportunity to participate in the employee equity program.
Urban Sports membership and BVG (public transport) subsidy, and company pension plan.
A diverse and vibrant international environment with 30+ nationalities.