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Jobs/Research Engineer Role/relationrx - Research Engineer
relationrx

relationrx - Research Engineer

London, United Kingdom, Hybrid1mo ago
In OfficeMidEMEACloud ComputingOil & GasResearch EngineerPythonTraining DevelopmentGCPAWSDocker

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Requirements

• BSc/MSc/PhD in CS/ML/Engineering (or related), 2+ years industry experience. • Solid understanding of algorithms, data structures, complexity. • Proficiency in Python with clean, maintainable coding practices. • Familiarity with PyTorch and common scientific Python libraries. • Solid understanding of ML fundamentals and modern deep learning. • Experience training, evaluating, and iterating on models. • Familiarity with AWS/GCP, Docker, Kubernetes, and CI/CD. • Familiarity with orchestration tools (Airflow/Prefect) and model-serving frameworks. • Exposure to biology/bioinformatics or ML for scientific domains. • PERSONALLY, YOU ARE: • A collaborative team player, able to work and communicate effectively with stakeholders from diverse backgrounds and technical abilities • Passionate about science and making a real difference for patients • Self-motivated, curious and driven to succeed • User-minded: you think about how datasets and models will be used downstream • Comfortable working independently and taking initiative • Relation is a committed equal opportunities employer. • RECRUITMENT AGENCIES: Please note that Relation Therapeutics does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.

Responsibilities

• Work with interdisciplinary teams to solve challenges across data engineering, ML engineering, and software engineering. • Build and improve data processing, transformation, and loading systems to support model training and inference at scale. • Develop and maintain high-quality research and production codebases to enable rapid, reproducible experimentation. • Advance our software engineering practices by optimising systems, streamlining pipelines, and improving robustness across workflows.

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