• 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.

Role Description We are seeking a Computer Science Subject Matter Expert to contribute to the development of next-generation AI systems by applying deep technical expertise to real-world computer science challenges. In this role, you will analyze, evaluate, and create high-quality technical content that enhances the reasoning, accuracy, and performance of AI models. Prior experience in Artificial Intelligence is not required—your expertise in Computer Science is the primary qualification. Key Responsibilities - Technical Analysis & Problem Solving - Analyze and evaluate complex computer science problems, solutions, and methodologies across various domains. - Assess technical approaches and recommend well-structured, scalable, and efficient solutions based on industry best practices. - Ensure technical accuracy, logical consistency, and clarity in all deliverables. - Content Development & Documentation - Author, review, and refine technical documentation, including system design documents, research papers, technical specifications, and architecture write-ups. - Develop real-world computer science scenarios, datasets, and case studies to support AI model training and evaluation. - Create structured technical explanations that improve AI understanding and reasoning. - AI Model Evaluation - Review AI-generated responses for technical correctness, completeness, and adherence to computer science principles. - Identify inaccuracies, inconsistencies, and opportunities for improvement while providing constructive feedback. - Support the evaluation and continuous enhancement of AI-driven tools through domain expertise. - Collaboration & Quality Improvement - Work closely with project coordinators and subject matter experts to refine task guidelines and improve data quality. - Provide clear written and verbal feedback to enhance AI model relevance, robustness, and performance. - Collaborate in a remote environment while maintaining high-quality deliverables and documentation. Qualifications - Bachelor's, Master's, or PhD in Computer Science or a related field. - Strong understanding of core computer science concepts, including algorithms, data structures, operating systems, databases, networking, and software engineering. - Experience writing technical documentation, research papers, system design documents, or technical reports. - Excellent analytical, problem-solving, and critical thinking skills. - Strong written and verbal communication skills with the ability to explain complex technical concepts clearly. - Ability to work independently and deliver high-quality work in a remote environment. Preferred Qualifications - Expertise in one or more specialized domains such as: - Algorithms & Data Structures - Distributed Systems - Computer Networks - Databases - Artificial Intelligence & Machine Learning - Cloud Computing - Cybersecurity - Experience participating in technical reviews, research projects, or open-source contributions. - Proven ability to simplify complex technical concepts for both technical and non-technical audiences. - Familiarity with current trends and emerging technologies in computer science. Must-Have Skills - Computer Science - Algorithms & Data Structures - System Design - Technical Documentation - Problem Solving Good-to-Have Skills - Distributed Systems - Databases - Networking - Artificial Intelligence - Research & Publications - Open Source Contributions - Software Engineering - Technical Reviews