About Valvian

Valvian is a Lisbon-based biotechnology company developing next-generation therapeutics for cancer and age-related disorders. We exist because cancer and the diseases of ageing remain among the largest sources of human suffering, and the conventional drug-discovery playbook has been slow to close the gap. Our bet is that better therapeutics will come from a tighter integration of disease biology, translational biomarkers and computational pipelines.

In oncology, our focus is solid tumours, where antigen complexity, the tumour microenvironment and immune evasion demand careful modality selection, safety-by-design and biomarker-driven translation. Our portfolio includes antibodies and cell therapies designed specifically for solid-tumour constraints. In age-related disorders, we work on the system-level biology of ageing: immune ageing, chronic inflammation, progressive tissue dysfunction and cellular senescence, with biomarker anchors that make each programme development-ready.

About the position

The Senior ML/AI Scientist will be the technical partner driving the architecture and ML research behind Valvian’s Computational Biology & AI platform. This role co-owns the design of the data and reasoning architecture that will support target discovery in oncology and age-related diseases. While the broad architecture and core principles are set, there is genuine scope for research and iteration in how they are best realised – this role will play a key part in that process.

You will partner directly with the Head of Computational Biology & AI and with senior scientific leadership to define how the platform is built, what it predicts, and how it integrates with the company’s therapeutic programmes.



The platform is being built from day one. The decisions made in the first months will shape how the company prioritises candidates, integrates experimental data and translates biological insight into therapeutic programmes for the next decade. If you have ever wanted to work on something genuinely new, before it is named, published or commoditised, this is that opportunity.

We are looking for someone who works from first principles, challenges briefs and tooling with evidence, and treats model outputs as scientific inputs rather than black-box predictions. Part of this role also involves contributing to a confidential research initiative in a novel scientific domain; details available under NDA after an initial conversation.

Key Responsibilities

1. AI Platform Architecture & Technical Research

• Lead the design and development of an internal AI-enabled research platform combining structured data, machine learning and advanced analytical workflows.
• Work closely with senior scientific and computational leaders to translate complex domain knowledge into scalable data models, platform components and computational workflows.
• Contribute to proof-of-concept development, technical validation and tooling assessments across data infrastructure, ML frameworks and knowledge-based systems.
• Establish the architectural principles and implementation patterns that allow the platform to scale as internal data, use cases and technical capabilities mature.

2. Reasoning and ML layer

• Contribute to the evaluation and integration of domain-specific ML approaches and foundation models into the platform’s inference layer.
• Drive the inference layer that bridges structured biological knowledge with predictive ML, with particular attention to explainability and hypothesis generation as first-class outputs.
• Iterate on architectural choices, informed by emerging data and science.

3. Scientific and technical partnership

• Operate as an autonomous scientific and technical partner to the Head of Computational Biology & AI, co-deciding technical direction.
• Engage actively with biological collaborators (advisors and R&D) to translate biological insight into computational structure, and computational outputs back into testable biological hypotheses.
• Validate platform outputs against Valvian’s existing therapeutic programmes.

4. Engineering foundations

• Build and maintain solid technical foundations from the start: version control, environment management, reproducibility, modular code, testing where it matters.
• Produce clear technical documentation that lets the rest of the team understand and iterate on what you have built.
• Work in modern cloud-native infrastructure, with attention to GDPR-compliance and data sovereignty from day one.

Required Qualifications

• Demonstrable track record of designing and shipping ML systems for scientific or research applications, with public evidence (publications, patents, open-source contributions, conference talks, or equivalent industrial research output).
• Direct experience working with heterogeneous data sources and the engineering required to integrate them in a principled way.
• Strong programming skills and practical experience building reproducible computational workflows.
• Senior individual contributor or principal-scientist level profile, typically with substantial post-PhD or equivalent industry experience.

Preferred Experience

• PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Data Science or a related field is a plus
• Experience working with one or more of the following kinds of approaches: knowledge graphs and graph ML, neuro-symbolic reasoning, foundation models in biology, agentic AI systems and LLM-augmented workflows, hybrid systems combining learned and structured representations.
• Familiarity with cloud-native ML infrastructure and platform tooling is appreciated.
• Experience working closely with research scientists where requirements shift as the science evolves.
• Prior experience in biotech, drug discovery or translational research is a plus, but deep biological expertise is not mandatory.

Soft Skills

• Discovery orientation: comfortable working from first principles with incomplete specification. Enjoys the phase where requirements emerge through building and learning, rather than executing pre-defined deliveries.
• Critical thinking: challenges briefs, tooling, and methods with evidence. Does not default to consensus.
• Ownership: drives decisions end-to-end, from definition to validation. Owns outcomes, not just deliverables.
• Bias for iteration: ships rough first versions to learn, then refines smartly. Comfortable with imperfection in early iterations, disciplined about converging.

What We Offer

• Be the first architect on Valvian’s AI platform: foundational decisions are yours to make, with no inherited tech debt.
• Direct collaboration with senior scientific and computational leadership on architectural and scientific direction.
• Competitive compensation including equity.
• Lisbon-based hybrid work (in-office presence calibrated to project needs), with international collaborations and ecosystem exposure.
• Opportunity to shape a category from inception.