We are looking for a Machine Learning Engineer who can facilitate the productization, deployment, and maintenance of machine learning algorithms in production to join our data science team in Madrid. By working at Frontiers, you will play a key role in shaping the future of science and academic publishing. You will be joining a thriving company where your contribution will have an immediate effect on the way science is evolving.
- Development, optimization, productization and performance monitoring of machine learning models and pipelines in collaboration with our data science team, software development teams and devops.
- Technological review of machine learning methods and technologies and dissemination of good machine learning engineering practices.
- MSc in Computer Science or similar.
- Strong proficiency in Python and familiarity with libraries such as Scikit-learn, Pandas, NumPy, and SciPy.
- Experience in deploying Machine Learning models in production.
- Ability to effectively communicate complex ideas to other members of the team and deal with organizational complexity.
- Experience working with large structured and unstructured datasets (text, images, relational data).
- Experience working with data pipelining tools like Azure Data Factory, Apache Airflow/Beam.
- Experience with some or all of the following:
- REST APIs
- SQL and noSQL (e.g. Elasticsearch) databases
- Azure (or other cloud provider)
- Containerization technology (Docker/Kubernetes)
- Machine Learning and Deep Learning frameworks (Tensorflow/Keras, PyTorch, ML.NET)
The working language is English. We offer a modern office, a friendly and international working environment, team building/sport activities, and monthly social events. You will operate in small teams with freedom for independent work and fast decision making.
How to apply
Please submit your application in English.
Applicants must be Spanish or EU citizen, or have a valid Spanish work permit.
Agencies must first contact email@example.com and confirm agreement to our T&C’s, failing which any exclusivity and/or candidate representation right will be considered to be waived.