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

<p>As a rapidly expanding start-up at the intersection of agriculture and technology,<br>
ScreenSYS is seeking a self-motivated, experienced, passionate, and team-oriented<br>
professional to join our company in the role of Senior Machine Learning Developer.<br>
This role is ideal for a seasoned professional with a strong background in machine<br>
learning, deep learning, and project leadership. The candidate will work closely with a<br>
team of experts in plant biology, data science, machine learning, and laboratory automation to develop cutting-edge AI solutions that drive innovation in agribusiness<br>
and beyond.</p>
<h2>Tasks</h2>
<ul>
<li><strong>Machine Learning Development &#x26; Optimization:</strong> Lead the design,<br>
development, and optimization of advanced machine learning models (e.g.<br>
Multi-Modal Foundation Models) to solve complex problems in agriculture,<br>
such as protocol optimization to reprogram haploid plant microspores.</li>
<li><strong>Project Leadership:</strong> Take ownership of end-to-end machine learning projects,<br>
from ideation and research to deployment and optimization, ensuring<br>
alignment with business goals.</li>
<li><strong>Team Mentorship:</strong> Guide and mentor a team of machine learning engineers<br>
and data scientists, fostering a culture of innovation, collaboration, and<br>
continuous learning.</li>
<li><strong>Cross-Functional Collaboration:</strong> Work closely with interdisciplinary teams,<br>
including plant biologists, data scientists, and software engineers, to integrate<br>
AI solutions into real-world applications.</li>
<li><strong>Performance Optimization:</strong> Optimize models for efficiency, scalability, and<br>
deployment in production environments, ensuring robustness and reliability.</li>
<li><strong>Research &#x26; Innovation:</strong> Stay at the forefront of AI research, exploring and<br>
implementing cutting-edge methodologies to enhance model performance<br>
and scalability.</li>
</ul>
<h2>Requirements</h2>
<ul>
<li><strong>Educational Background:</strong> A Master’s or Ph.D. degree in STEM fields, such as<br>
computer science, mathematics, statistics, or a related discipline.</li>
<li><strong>Specialized Experience:</strong><br>
- 5+ years of experience in machine learning and deep learning, with a proven track record of developing and deploying large-scale models.<br>
- Demonstrated experience in training and fine-tuning foundation models (e.g., GPT, BERT, Vision Transformers, or similar).<br>
- Strong leadership experience, including leading machine learning projects and mentoring teams.</li>
<li><strong>Technical Skills:<br>
-</strong> Proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or JAX.<br>
- Experience with distributed training techniques and frameworks (e.g., Horovod, DeepSpeed).<br>
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and<br>
containerization tools (e.g., Docker, Kubernetes).<br>
- Strong understanding of software engineering best practices, including<br>
version control (Git), CI/CD pipelines, and agile methodologies.<br>
- Familiarity with database management systems (relational and NoSQL).</li>
</ul>
<p><strong>Advantageous Skills:</strong></p>
<ul>
<li>A foundational understanding of biology or life sciences, enabling effective<br>
collaboration with domain experts.</li>
<li>Knowledge of reinforcement learning or generative models.</li>
</ul>
<h2>Benefits</h2>
<ul>
<li>A permanent position within a dynamic and exciting R&#x26;D environment driven<br>
by a start-up spirit.</li>
<li>Competitive salary and annual performance bonus.</li>
<li>Interdisciplinary, multinational, and creative working environment</li>
<li>An inclusive, interdisciplinary, and multinational working environment.</li>
</ul>
<p>To be considered for the role, please attach a "cover letter" answering the following questions:</p>
<ul>
<li>Multimodal Modelling: Do you have experience with mutli-modal modelling, combining computer vision with related metadata? If so, how did you approach it</li>
<li>Image Data Quality Assurance: Do you have experience with data quality assurance, and which strategy would you recommend for image or meta data?</li>
<li>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)?</li>
<li>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?</li>
</ul>
<p>Join us at ScreenSYS and be part of a team that’s revolutionizing agriculture through technology.</p>
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