Senior Translational AI Scientist/Engineer

A pioneering biotech company dedicated to advancing longevity and regenerative medicine. By harnessing breakthroughs in cellular biology, AI-driven analytics, and bioengineering, we develop cutting-edge solutions to enhance health and extend lifespan. Our multidisciplinary team of scientists, data engineers, and researchers collaborates to drive innovation in ageing research, biomarker discovery, and precision health technologies.

We are seeking a scientist/engineer with hands-on expertise in building generative and agentic AI systems and a strong foundation in target discovery, drug discovery and translational science. You will design and deploy AI-enabled systems that take a target and produce decision-grade, evidence-backed recommendations — from mechanism hypotheses to validation design — with every claim grounded in retrievable evidence and every gap stated explicitly, and that assess how likely a human-derived signal is to hold up in the lab and beyond.

This role is ideal for someone who would rather build production AI systems for translational science than only run analyses, and who has enough hands-on biology to know when a recommendation is scientifically sound and when it merely reads well. You will own the engineering of these systems end to end, from retrieval and orchestration to evaluation and deployment, and work as a peer with the scientists who act on their outputs.

We’re looking for an experienced Scientist/Engineer to join our small, but powerful cross-functional team of engineers and biologists. You’ll contribute to projects that deliver value in fast, iterative cycles, while learning how to adapt in a dynamic startup environment where priorities can shift quickly.

Basic Qualifications

• Education & experience: PhD with 2+ years of relevant experience, or Master’s degree with 5+ years, or Bachelor’s degree with 7+ years, in Computer Science, Computational Biology, Biology, Translational Science, or a related field.
• We welcome both scientist-first candidates with a demonstrated ability to build production-quality AI workflows and engineer-first candidates with strong translational-biology expertise and scientific judgment.

Preferred Technical Qualifications

• AI & agentic systems: Hands-on experience building and deploying LLM-based agentic systems in production or production-like environments, including tool use, retrieval across structured and unstructured data, multi-agent orchestration, structured outputs, provenance tracking, and cost/latency optimisation.
• AI evaluation & reliability: Experience designing and running evaluations for AI systems, including reference datasets, automated metrics, and regression testing, with a strong understanding of interpretability and scientific reliability in decision-critical environments.
• Software engineering: Strong fundamentals in Python, testing, version control, API design, data modelling, and reproducible, auditable workflows, including orchestration, documentation, and CI/CD.
• Biomedical data & AI: Experience integrating multimodal biological data — including omics, phenotypes, perturbation data, text, and scientific literature — using AI or other model-based approaches.
• Research infrastructure: Experience with relational and graph databases, cloud environments, containers, pipeline orchestration, and programmatic access to large public biological databases through APIs or bulk data.

Preferred Scientific Qualifications

• Translational research: Hands-on or wet-lab research experience in one or more of the following areas:

  • In vivo pharmacology or disease models
  • Target validation
  • Disease biology
  • Functional genomics
  • Chemical biology
  • Translational science

  Candidates should have sufficient scientific judgment to assess whether a proposed experiment, mechanism, or line of evidence is scientifically sound.

• Biological evidence & resources: Working knowledge of resources covering target and precedent evidence, including model-organism phenotype databases, chemical probe and druggability resources, drug and clinical-trial databases, pathway resources, and perturbation datasets — together with the judgment to understand when each source is reliable.
• Preclinical-to-clinical translation: Familiarity with the evidence connecting preclinical findings to clinical outcomes, including the role of genetic support and research on animal-to-human translation.
• Scientific rigour: Strong evidence-based thinking. You distinguish absence of evidence from evidence against, report coverage alongside conclusions, and prefer clearly identifying an evidence gap rather than filling it with plausible but unsupported assumptions.
• Cross-functional collaboration: Ability to work effectively across scientific and engineering teams, with strong written and verbal communication, self-motivation, and independence.

Also Valued

• Aging biology / geroscience: Background in aging biology or geroscience, including healthspan endpoints and aging-specific study designs.
• Proteomics & biomarkers: Experience with proteomics, cross-species biomarker translation, or biomedical ontologies.
• CRISPR & screening: Familiarity with CRISPR and perturbation resources, as well as pooled or arrayed screening approaches.