Mid-level Machine Learning Engineer, Bioinformatics
DeepLab, Greece

Deeplab combines high-end software technologies with state-of-the-art machine learning and data science research to provide services and products to deal with challenging real-life problems. This is a chance for you to join a dynamic, rapidly evolving ML/AI organization committed to research, innovation and excellence with a culture that bridges the gap between academic rigor and entrepreneurial agility. We foster an environment where the methodical, research-driven pursuit of knowledge meets innovative, fast-paced decision-making. This synergy fuels our pursuit of excellence and innovation in practical real-life problems.

We are currently expanding our ML team for bioinformatics solutions, part of an international research and development partnership with industrial partners in the general fields of drug discovery and cancer research. Our team deals with disruptive projects from ideation to productization, combining innovative ML concepts with cutting-edge production technologies. You will be joining an interdisciplinary team comprising passionate ML engineers with backgrounds in both research and software development.

We seek outstanding and enthusiastic Mid-level machine learning engineers with an exceptional record, who share our passion for problem-solving applied to tough diverse bioinformatics industrial/research problems. You will have the opportunity to contribute in creating new Ramp;D while employing cutting-edge machine learning, applied across challenging projects with top-level academic/industrial partners and find tremendous opportunities to further develop yourself together with the team.

Mid-level Machine Learning Engineer candidates are expected to have experience in machine learning, either acquired through previous practical experience in a relevant industry/research position, or showcased by their exceptional academic achievements on a related postgraduate degree.

Requirements

1. BSc/MSc (Electrical amp; Comp. Eng., Machine Learning, CS), and experience in implementing and benchmarking machine learning algorithms for real-world data.
2. 2+ years hands-on industrial or academic experience in machine learning projects.
3. Proven hands-on research experience employing machine learning, underscored by publications in esteemed journals or conferences.
4. Familiar with development processes using Linux, Git, Docker etc.
5. Candidates should be proficient in Python, with experience developing, testing, and maintaining production-grade machine learning applications using frameworks such as TensorFlow or PyTorch.
6. Nice to have: familiarity with concepts in bioinformatics, computational biology, drug discovery, cheminformatics, and molecular biology.
7. Nice to have: familiarity with chemical informatics libraries and tools such as RDKit or Open Babel.
8. Nice to have: basic understanding of molecular modeling, docking simulations, or other computational techniques relevant to drug design.
9. Nice to have: experience with large scale cluster computing for machine learning modeling.
10. Ability to build strong relationships in a challenging international environment.
11. Excellent written and oral communication skills; ability to present complex analyses in a clear, concise and actionable manner.

1. Develop machine learning algorithms and infrastructure.
2. Write production-grade ML code, perform experimentation and benchmarking.
3. Statistical modeling and data analytics.
4. Apply deep learning techniques to bioinformatics or other demanding challenges, e.g. enhancing drug discovery pipelines.
5. Manage, curate, and preprocess diverse biological datasets to ensure quality and consistency for downstream machine learning tasks.
6. Ensure algorithms and models are optimized for both computational efficiency and scalability to handle large-scale biological datasets.
7. Stay updated with the latest advancements in machine learning methodologies such as transfer learning, semi-supervised learning, etc as well as bioinformatics, and computational biology to ensure Deeplab's solutions remain at the cutting edge.
8. Collaborating with a cross-functional international team that brings together the best in class AI and Software Engineering to tackle the scientific challenges Deeplab is looking to find solutions for.

Benefits

1. Supplementary private health insurance.
2. Flexible working hours and remote work from home.
3. Working in high-end AI-tech and having an impact in real-world applications.
4. Budget for home office equipment to ensure a productive and efficient workspace.
5. Personal growth opportunities through internal knowledge sharing and self-development budget.
6. Newly designed inspirational and fresh working environmen

Job Rewards and Benefits