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.
Designing and operating cloud - based MLOps capabilities supporting analytical and generative AI models. Collaborating with data science and business teams for high - impact AI solutions.
Machine Learning Engineer analyzing data structures and developing ML models for customer profiling in Azerbaijan. Collaborating on probabilistic modeling and data quality improvement.
Machine Learning Engineer at HackerRank working on integrity systems to improve model quality. Collaborating on strategies for new signals like audio analysis and behavioral anomalies.
Machine Learning Engineer developing integrity systems for assessing model quality at HackerRank. Collaborating on multimodal signal processing and improving model performance.
Architect designing enterprise - grade AI/ML architectures for Quantiphi. Leading AI applications and ML strategy with a focus on scalability, security, and integration.
Software Engineer for ML Infrastructure at Slack, architecting systems to support large scale AI deployment and reliability. Engage in deep systems engineering focusing on ML lifecycle and infrastructure scalability.
Machine Learning Engineer at Winnow developing AI solutions for food waste reduction. Collaborate with cross - functional teams and leverage cutting - edge technologies in food recognition.
Senior Engineer developing AI/ML solutions to enhance patient care at Edwards Lifesciences. Collaborating with cross - functional teams to deliver impactful technologies in healthcare.
Machine Learning Engineer designing and deploying machine learning models for DXC Technology. Collaborating with data scientists and optimizing solutions for impactful results.
Senior Machine Learning Engineer at APS leading MLOps initiatives and collaborating across teams. Designing and implementing scalable machine learning solutions with a focus on real - time decision - making.