Benefits:

• International product and innovation-driven team with rich expertise in Computer Vision and Reinforcement Learning as well as distributed training, data engineering, ML ops and cloud architectures

• Working with the latest technologies at the interface between research and industry (enliteAI is an ELISE EU research network Organizing Node)

• Personal growth: Receive continuous training and education opportunities. Budget and time allotment for the pursuit of individual R&D projects, training or conference participations

• Flexible work models: Remote work, an office in Vienna's 1st district and minimal core hours

Experience Requirements:

• 3+ years of work experience in data-driven environments

• Passionate about everything related to AI, Machine Learning and Computer Vision

Other Requirements:

• Python programming skills, emphasis on data engineering and distributed processing (e.g. Flask, Postgres, SQLalchemy, Airflow)

• Proficiency in working with databases and data storage solutions

• Experience with Kubernetes and Docker (Helm, Terraform, Amazon Kubernetes Service)

• Familiarity with cloud environments (AWS, Gcloud, Azure)

• Used to mature workflows in software development (Git, issue management, documentation, unit testing, CI/CD)

• Fluent in English both spoken and in written language.

• Valid work permit for Austria

Responsibilities:

• Collaborate with our machine learning and backend engineers to design and manage scalable processing pipelines in productive environments.

• Implement robust processing flows and I/O-efficient data structures, powering use cases such as road surface analysis, sign detection and localization on large volumes of point cloud and imagery data.

• Design and manage relevant database schemes in close collaboration with backend engineering.

• Create and maintain comprehensive documentation of the processing pipelines, database schemes, configuration and software architecture.

Benefits:

• International product and innovation-driven team with rich expertise in Computer Vision and Reinforcement Learning as well as distributed training, data engineering, ML ops and cloud architectures

• Working with the latest technologies at the interface between research and industry (enliteAI is an ELISE EU research network Organizing Node)

• Personal growth: Receive continuous training and education opportunities. Budget and time allotment for the pursuit of individual R&D projects, training or conference participations

• Flexible work models: Remote work, an office in Vienna's 1st district and minimal core hours

Experience Requirements:

• 3+ years of work experience in data-driven environments

• Passionate about everything related to AI, Machine Learning and Computer Vision

Other Requirements:

• Experience with Kubernetes and Docker (Helm, Terraform, Amazon Kubernetes Service)

• Familiarity with cloud environments (AWS, Gcloud, Azure)

• Python programming skills, emphasis on backend related technologies (e.g. Flask, Postgres, SQLalchemy)

• Used to mature workflows in software development (Git, issue management, documentation, unit testing, CI/CD)

• Data engineering knowledge (SQL databases, distributed systems)

• Fluent in English both spoken and in written language.

• Valid work permit for Austria

Responsibilities:

• Collaborate with our machine learning engineers and data engineers to publish our models into processing pipelines and deploy to productive environments.

• Design and manage our GPU & CPU server infrastructure, from on-prem Kubernetes clusters to cloud deployments.

• Manage and orchestrate the data pipelines and data storage systems and associated synchronization processes for model training and execution.

• Own our CI/CD pipelines (based on Gitlab)

• Establish monitoring of model, pipeline and infrastructure health. Set up logging to capture relevant information for debugging and auditing.

Benefits:

• Marie Skłodowska-Curie Action scheme advantages

• Participation in web seminars

• Attendance at yearly meetings and international congresses

• Work at a modern office in Vienna

Education Requirements:

• Master's degree in bioinformatics/computational biology

Other Requirements:

• Fluent in R or Python

• Understanding of cellular and molecular biology

• Experience with SQL, JSON, XML

• Statistical analysis of large datasets

• Experience with biological databases (NCBI, Ensembl, UniProt, GEO, ArrayExpress)

• Knowledge of biological ontologies

• Knowledge of graph theory

• Experience with biological network modeling

Responsibilities:

• Meta-analysis of omics data

• Generation of a network-based molecular model

• Identification of compounds interfering with disease pathobiology

• Identification of drug combinations targeting synthetic lethal interactions