Role Description We are looking for an experienced and pragmatic Senior Computer Vision Engineer to take ownership of key components of our production computer vision pipeline. This is a hands-on role for an engineer who thrives on solving complex challenges and is passionate about building robust, reliable systems. - Maintain, optimize, and ensure the reliability of our complex, multi-stage computer vision pipeline, which includes models for semantic segmentation, object detection, image classification, and optical flow estimation. - Set the technical vision and roadmap for specific parts of the CV pipeline, making key architectural and algorithmic decisions to drive performance and accuracy. - Lead the adaptation of existing models and the development of new components for upcoming projects, translating business needs into technical solutions. - Oversee the entire data lifecycle for your models, from defining clear annotation guidelines to managing data quality and iterating on the feedback loop with our in-house annotation team. - Write clean, maintainable, and robust production-level Python code, adhering to strong software engineering best practices. - Manage model training, experimentation, and evaluation on our on-premise Linux server infrastructure. Qualifications - Master's degree or PhD in Computer Science, or a related field. - 5+ years of professional experience building and deploying computer vision models in a production environment. - Deep, hands-on expertise with PyTorch. - Proven experience across a range of computer vision tasks (e.g., object detection, semantic segmentation, image classification). - Exceptional software engineering fundamentals with a focus on writing clean, testable, and efficient Python code. - High proficiency in a Linux environment is mandatory. You should be comfortable and efficient using shell scripting, SSH, and command-line interfaces for daily work. - Demonstrated experience working with data annotation pipelines, defining guidelines, and ensuring data quality. - Excellent problem-solving skills and the ability to work independently and take ownership of projects. - Willingness to embrace new ideas and feedback, work collaboratively with others, and continuously seek improvement. Requirements - Experience working with large-scale datasets (TBs) and building efficient data processing pipelines. - Experience with model optimization techniques (e.g., quantization, pruning, TensorRT/ONNX conversion). - Experience mentoring junior engineers or providing technical leadership. Benefits - Colleagues who strive for excellence while using the latest technologies. - Educational budget for your personal growth. - Above-average corporate pension plan. - Work from home up to 5 days a week. - Join more than 90 different nationalities in a truly international and diverse working environment. Company Description AUTO1 Group is Europe's leading digital automotive platform. Within AUTO1, our dedicated Computer Vision team is at the forefront of innovation, building the next generation of technology for the used car market. Our mission is to create a new, objective standard for vehicle assessment. We develop and deploy proprietary devices that automatically inspect used cars for exterior damages like scratches, dents, and other imperfections, bringing unprecedented transparency and efficiency to the industry. We are a focused, engineering-driven team building complex, real-world systems that merge hardware, software, and deep learning.

• Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition • Fine-tune and deploy open-source vision-language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) for product understanding, zero-shot classification, and scene reasoning • Build vision-language-action pipelines that translate visual understanding into downstream decisions • Take state-of-the-art models and optimize them for edge deployment through quantization, pruning, and architectural optimization • Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios • Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's production-ready • Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Role Description We're looking for a Principal Computer Vision Engineer to lead the design, development, and scaling of our Digital Biological Assessment systems — one of the most advanced drone-based computer vision platforms in agriculture R&D. You will process hundreds of terabytes of aerial imagery annually, build machine learning models that replace manual field assessments, and lead a team of engineers pushing the boundaries of what's possible in precision agriculture. What You'll Do - Lead a team of computer vision engineers, setting technical direction and developing talent - Architect and maintain end-to-end image analysis systems — from drone capture through ML inference to data output - Write production-quality Python code for image processing, photogrammetry pipelines, and ML model deployment - Design and train machine learning models for plant counting, height measurement, health assessment, and disease/weed detection from aerial imagery - Manage AWS cloud infrastructure supporting large-scale image processing workloads - Drive the integration of emerging technologies (large vision-language models, foundation models) into the assessment pipeline - Collaborate cross-functionally with R&D biologists, IT, and operational teams to translate scientific needs into technical solutions - Communicate the team's vision and results to stakeholders at all levels, including executive leadership Qualifications - Ph.D. or M.Sc. in Computer Science, Machine Learning, Remote Sensing, or related field - 10+ years of experience in computer vision and data engineering, with deep ML expertise - 3+ years of people management experience leading technical teams - Strong hands-on Python and Linux skills — you still write code and make architectural decisions - Experience with photogrammetry (Metashape, Pix4D, or equivalent) and/or drone-based remote sensing - Proven ability in building and operating large-scale image processing pipelines on AWS - Excellent communication skills — you can explain complex technical concepts to non-technical stakeholders and influence across organizational boundaries - Fluent English (written and spoken) Requirements - Experience in agriculture, crop science, or precision farming applications - Background in multispectral/hyperspectral sensor data analysis - Experience with large vision-language models or foundation models for vision - Experience in fast-moving startup environments Benefits - Unmatched scale: Few teams anywhere process computer vision at this volume — hundreds of terabytes of drone imagery per year across global field trials - Real-world impact: Your work directly accelerates the development of crop protection products that help feed the world sustainably - Innovation freedom: Strong leadership support, dedicated budget, and room to explore cutting-edge AI/ML approaches - Flexible work: Remote-friendly with occasional travel to Basel (1–2x/year). We prioritize the best talent regardless of location - Growth path: Clear trajectory to R&D or Digital leadership roles at the leadership team level

• Research, develop, and validate computer vision algorithms (object detection, segmentation, anomaly detection, tracking, few shot approaches) for automated and generalised defect and anomaly detection. • Train and optimize deep learning models (e.g., CNNs, YOLO, Vision Transformers) for high-accuracy and high-speed computer vision-based data analysis. • Develop models to identify defects based on variations from normal images. • Design and evaluate vision‑language models to interpret images and support anomaly‑detection tasks. • Stay up to date with the latest advancements in AI, Machine Learning, and Computer Vision literature to maintain competitive advantage.