Senior Machine Learning Developer: Foundation Models & AI Innovation (f/m/d)

About ScreenSYS

ScreenSYS is a rapidly expanding start-up at the intersection of agriculture and technology. We are seeking a self-motivated, experienced, passionate, and team-oriented professional to join our company in the role of Senior Machine Learning Developer. This role is ideal for a seasoned professional with a strong background in machine learning, deep learning, and project leadership. The candidate will work closely with a team of experts in plant biology, data science, machine learning, and laboratory automation to develop cutting-edge AI solutions that drive innovation in agribusiness and beyond.

Responsibilities

• Machine Learning Development & Optimization: Lead the design, development, and optimization of advanced machine learning models (e.g., Multi-Modal Foundation Models) to solve complex problems in agriculture, such as protocol optimization to reprogram haploid plant microspores.
• Project Leadership: Take ownership of end-to-end machine learning projects, from ideation and research to deployment and optimization, ensuring alignment with business goals.
• Team Mentorship: Guide and mentor a team of machine learning engineers and data scientists, fostering a culture of innovation, collaboration, and continuous learning.
• Cross-Functional Collaboration: Work closely with interdisciplinary teams, including plant biologists, data scientists, and software engineers, to integrate AI solutions into real-world applications.
• Performance Optimization: Optimize models for efficiency, scalability, and deployment in production environments, ensuring robustness and reliability.
• Research & Innovation: Stay at the forefront of AI research, exploring and implementing cutting-edge methodologies to enhance model performance and scalability.

Requirements

• Educational Background: A Master’s or Ph.D. degree in STEM fields, such as computer science, mathematics, statistics, or a related discipline.
• Specialized Experience:
  • 5+ years of experience in machine learning and deep learning, with a proven track record of developing and deploying large-scale models.
  • Demonstrated experience in training and fine-tuning foundation models (e.g., GPT, BERT, Vision Transformers, or similar).
  • Strong leadership experience, including leading machine learning projects and mentoring teams.
• Technical Skills:
  • Proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or JAX.
  • Experience with distributed training techniques and frameworks (e.g., Horovod, DeepSpeed).
  • Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes).
  • Strong understanding of software engineering best practices, including version control (Git), CI/CD pipelines, and agile methodologies.
  • Familiarity with database management systems (relational and NoSQL).

Advantageous Skills

• A foundational understanding of biology or life sciences, enabling effective collaboration with domain experts.
• Knowledge of reinforcement learning or generative models.

Benefits

• A permanent position within a dynamic and exciting R&D environment driven by a start-up spirit.
• Competitive salary and annual performance bonus.
• Interdisciplinary, multinational, and creative working environment.
• An inclusive, interdisciplinary, and multinational working environment.

Application Instructions

To be considered for the role, please attach a "cover letter" answering the following questions:

• Multimodal Modelling: Do you have experience with multi-modal modelling, combining computer vision with related metadata? If so, how did you approach it?
• Image Data Quality Assurance: Do you have experience with data quality assurance, and which strategy would you recommend for image or meta data?
• Computer Vision Depth: Do you have hands-on experience training and deploying deep learning models for image segmentation or object detection? If so, which frameworks (e.g. Detectron2, MMDetection, YOLOv8)?
• Self-supervised or Label-efficient Learning: Do you have experience with self-supervised pretraining, semi-supervised, or active learning approaches? If so, in what setting?

Join us at ScreenSYS and be part of a team that’s revolutionizing agriculture through technology.