Machine Learning Research Engineer
Avaron AB Göteborg
About the Company
Avaron AB is a growing consultancy that matches your expertise with the market's most interesting assignments, offering a platform where your professional development is central.
About the Assignment
You will join an innovative research-focused environment working at the intersection of AI, engineering, and drug discovery. In this role, you will help develop and scale modern AI systems that support the training and inference of large-scale deep learning models, while collaborating closely with researchers, software engineers, and domain experts in life sciences.
Job Description- Optimize training and inference workflows for large-scale deep learning models used in drug design.
- Design, conduct, and evaluate scientific experiments together with a multidisciplinary research team.
- Extend and maintain scientific Python codebases that support AI platforms and research workflows.
- Collaborate with data scientists, software engineers, and drug discovery specialists to develop robust engineering solutions.
- Contribute to platform development that enables the next generation of foundational chemical models.
- Degree in computer science, engineering, chemistry, physics, mathematics, or a related discipline, or equivalent industry experience.
- Hands-on experience training models with PyTorch or equivalent frameworks.
- Strong programming skills in Python.
- Experience developing sizable software solutions.
- Familiarity with scientific Python packages such as NumPy and Pandas.
- Ability to work effectively in multidisciplinary teams and contribute to a collaborative team culture.
- Experience with high-performance computing platforms and schedulers such as SLURM.
- Experience writing ETLs for large data sets.
- Background in life sciences or physical sciences.
- Research contributions to open-source projects, publications, or relevant online communities.
Application
Selections are made on an ongoing basis, so we recommend that you apply as soon as possible.
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