relationrx - Snr/Principal Machine Learning Scientist – Generative Modelling
Requirements
• Track record of impactful publications or open-source contributions in ML. • Experience working in interdisciplinary teams or applying ML in real-world settings • PERSONALLY, YOU • Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. • Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work. • Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect. • Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams. • Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes. • WORKING STYLE & CULTURE AT RELATION • At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting! • RECRUITMENT AGENCIES • Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs. • Relation is a committed equal opportunities employer.
Responsibilities
• Design and implement generative modelling approaches that learn intervention effects from diverse biological data, including single-cell perturbation experiments. • Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design. • Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies that identify the highest-signal next experiment. • Evaluate models not just for fit, but for causal coherence, mechanistic fidelity, and utility in guiding real-world interventions. • Communicate findings clearly across disciplinary boundaries, and contribute to high-impact publications. • PhD in ML, statistics, computer science, or a related quantitative field. • Deep expertise in generative modelling. • Strong foundations in probabilistic modelling, representation learning, or neural network architectures for structured or sequential data. • Excellence in Python and familiarity with scalable ML tooling and high-performance computing. • A disciplined approach to model evaluation, with experience designing experiments that go beyond standard benchmarks to test real-world utility. • Willingness and ability to engage deeply with biological data; prior experience with single-cell or perturbational datasets is a strong plus.
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