Senior Machine Learning Engineer developing deep learning models for radiological imaging applications. Focusing on designing and validating ML solutions to support accurate medical diagnoses with interdisciplinary teams.
About the role
- Senior ML Engineer developing and scaling multitemporal, multimodal models for Earth observation using satellite imagery at LiveEO. The role involves applied research and engineering with real-world impacts.
Responsibilities
- Drive the development of state-of-the-art ML systems that can learn from and reason about large volumes of satellite imagery.
- Identify and adapt SOTA approaches in remote sensing and foundation models (papers → prototypes → validated baselines), focusing on pragmatic wins under real 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 & preprocessing for high resolution SAR and optical imagery (normalization/calibration choices, tiling/chipping, pairing/co-registration sanity checks, sampling/augmentations) and drive dataset quality diagnostics.
- Build scalable training + evaluation pipelines in our stack (Databricks, PyTorch Lightning, MLflow), including experiment tracking, reproducibility, and systematic failure analysis across geographies and acquisition conditions.
- Deliver production-ready ML components (robust inference interfaces, model packaging, deterministic evaluation, monitoring signals/model cards) that downstream teams can depend on.
- Collaborate closely with product teams to ensure the models translate into business value and with the data annotation team to define labeling guidelines and close feedback loops on edge cases and quality.
Requirements
- Strong Python engineering fundamentals with clean, maintainable coding style.
- Deep experience with PyTorch and PyTorch Lightning.
- Experience implementing and training deep learning models at scale.
- Strong understanding of ML experimentation, versioning, and tracking via MLflow and Databricks.
- Strong CV fundamentals (representation learning, supervision strategies, evaluation design) and practical debugging/optimization skills.
- Hands-on experience with satellite imagery; strong preference for experience spanning optical and SAR.
- You take ownership and proactively push work forward.
- You communicate clearly and collaborate smoothly both within and across teams.
- Pragmatic mindset: You balance deep research with practical delivery.
Benefits
- Flexible working hours and hybrid work model - we trust our employees to get their work done while maintaining a healthy work-life balance.
- 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.
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