Jobbörse
Veröffentlicht am 27. Okt. 2026
Heidelberg, NeckarRemote
Speichern legt die Bewerbungsmappe an.
Sehen Sie, wie nah Ihr Profil an dieser Anzeige ist.
Gehalt (Schätzung)
Vollzeit-Referenzwert: 44.000 €–58.000 €/Jahr
HeiGIT (Heidelberg Institute for Geoinformation Technology)
As a nonprofit research institute, HeiGIT (Heidelberg Institute for Geoinformation Technology) bridges the gap between geoinformatics research and real-world applications, developing open-source solutions that address societal, environmental, and humanitarian challenges. Our Geoinformation for Humanitarian Aid team uses state-of-the-art machine learning and innovative digital technologies to support humanitarian organizations across the disaster management cycle, from risk assessment and preparedness to response. Working closely with our partners, we see firsthand how our work makes a difference on the ground.
We are looking for a Senior Machine Learning Engineer, with focus on GeoAI for Humanitarian Applications, to join our team in Heidelberg as soon as possible. The position is available full-time or part-time (50-100%) and requires at least 50% on-site presence in Heidelberg. The position is initially a two-year fixed-term contract with the option to extend.
Identify, design and evaluate ML and AI approaches for geospatial, Earth observation and unstructured data (e.g. multimodal data fusion), and decide with the team which methods best fit humanitarian challenges
Keep track of the state of the art in AI and turn new ideas into prototypes and products
Develop and deploy ML models and data pipelines that are reliable, ready for real-world use and released as open source
Help shape the technical direction of our work and share your methodological expertise with the team through coaching and co-learning
A Master's degree or PhD in Geoinformatics, Earth Sciences, Geography, Computer Science, Physics, Mathematics, or a related field, with several years (ideally 5+) of professional experience developing and deploying ML solutions in industry or applied, project-based settings
Strong knowledge of modern machine learning and deep learning methods, including experimental design, model evaluation, and the assessment of data quality, bias, and uncertainty
Strong software engineering and MLOps skills (e.g. Python, PyTorch and/or TensorFlow), including experience deploying ML models (e.g. with Docker)
Experience working with geospatial data (e.g. OpenStreetMap, street-level imagery, or remote sensing) and unstructured data, including LLM-based approaches
Experience in research or innovation-driven projects, with a structured, evidence-based approach to solving complex problems and exploring new methods
Very good English communication skills, a collaborative mindset, and enthusiasm for sharing knowledge, mentoring others, and contributing to meaningful societal impact
30 days of vacation (on a 5-day workweek) and additional days off - for true relaxation and more free time
Flexible work arrangements - with flex time and the option to work remotely part of the time. We expect you to work on-site at our Heidelberg office at least 50% of your working time
Freedom to shape your work & professional development - a high degree of autonomy, short decision-making paths, and personalized professional development opportunities
More for you - attractive subsidies for things like fitness, daycare, jobticket, and bike leasing, as well as capital-forming benefits, employer-sponsored retirement plan, and subsidized meals
A job with purpose and impact - topics at the intersection of science, education, and social relevance
Wir zeigen einen Auszug und einen Link je Anzeige, und den vollen Text, wenn die Quelle es erlaubt. Genannte Marken und Logos gehören ihren Inhabern.
WesternacherHeidelbergHybrid
Grobe Schätzung: etwa 55.000 €/Jahr
WesternacherHeidelbergHybrid
Schätzung: etwa 40.000 €/Jahr
WesternacherHeidelbergHybrid
Schätzung: etwa 40.000 €/Jahr
Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung
Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung
