Senior ML Engineer

📋 Job Overview

We are looking for a Senior ML Engineer (f/m/x) to build and scale computer vision systems for Earth observation across a range of problems: detection, segmentation, classification, change analysis, and geometric vision such as 3D reconstruction and image matching. You'll work primarily with very high resolution optical imagery and, increasingly, across modalities including synthetic aperture radar (SAR). You'll turn this data into reliable, production-grade capabilities that downstream products depend on, working across the full ML lifecycle from data and training through evaluation to deployment.

🏢 About LiveEO

LiveEO is a young, dynamic team that thrives on big challenges and fast learning cycles. We move quickly, stay curious, and genuinely enjoy building together. We're on a mission to break the \"curse of Earth Observation\": turning incredible satellite data into reliable, actionable decisions that people can trust and use in real operations.

🎯 The Role

As a Senior ML Engineer, you will develop state-of-the-art computer vision systems that extract reliable information from large volumes of satellite imagery, across a range of tasks and all the way to production. You'll work with very high resolution optical imagery and increasingly with synthetic aperture radar (SAR). An important part of this role is bringing models to where they run under tight constraints, particularly for LiveEO's Twinspector satellite programme.

✅ Key Responsibilities

• Build vision models across tasks: design, train, and iterate on models for detection, segmentation, classification, change analysis, and geometric vision (3D reconstruction, image matching/registration) on VHR satellite imagery.
• Research to production: identify and adapt state-of-the-art approaches across computer vision and remote sensing, focusing on pragmatic wins under real constraints.
• Push toward the edge: bring models to constrained and embedded targets for LiveEO's Twinspector programme, using model compression, quantization, and optimization for on-device and onboard inference.
• Build scalable pipelines: training and evaluation infrastructure across cloud and secure on-prem environments, with experiment tracking, reproducibility, and systematic failure analysis.
• Deliver production-ready components: robust inference interfaces, model packaging, deterministic evaluation, and monitoring.
• Collaborate and communicate: work with the data annotation function on labeling guidelines, with partner teams to turn model capabilities into validated deliverables, and with external researchers.

📌 Required Qualifications

• Strong computer vision and machine learning fundamentals (representation learning, supervision strategies, evaluation design) and practical debugging/optimization skills.
• Practical experience across multiple computer vision problem types (detection, segmentation, classification, change detection, or geometric vision), with real depth in at least one.
• 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.
• Comfortable working with researchers and presenting findings clearly and efficiently.
• Willingness to work on-site in Berlin and eligibility to obtain a German security clearance (Sicherheitsüberprüfung).

⭐ Desirable Experience

• Experience deploying models under constrained compute or on edge / embedded devices (model compression, quantization, optimization).
• Hands-on experience with satellite / remote-sensing imagery.
• PhD in remote sensing, computer science, physics, applied mathematics, or a related field.
• Distributed computing with Ray; workflow orchestration with Prefect (or similar).
• Cloud platforms (AWS) and/or secure on-prem / HPC experience (SLURM, Docker, DVC).
• Geospatial stack: GDAL, Rasterio, GeoPandas, STAC.
• PostgreSQL (or similar).
• Experience with SAR alongside optical imagery, or familiarity with geospatial foundation models / VLMs.
• Experience with post training VLMs (SFT, RLVR).
• Experience with synthetic data generation and sim2real / domain adaptation.

🎁 Benefits

• 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

🛂 Visa & Eligibility

Willingness to work on-site in Berlin and eligibility to obtain a German security clearance (Sicherheitsüberprüfung).