Orakl Oncology is pioneering a new paradigm in cancer drug development by building the world’s largest cohort of patient-derived organoid (PDO) avatars. Through our unique platform, we generate extensive multi-modal data from these avatars to discover and validate new oncology therapeutics with real-world patient relevance.

We are seeking a Senior AI Scientist in Computational Biology to develop cutting-edge AI and ML solutions that integrate diverse biological datasets and accelerate therapeutic discovery. This is a high-impact role embedded in a vibrant and multidisciplinary team of experimental biologists and engineers.

What You’ll Do

• Design and implement advanced machine learning algorithms for large-scale, multi-modal biological datasets, including genomics, transcriptomics and experimental PDO data.

• Develop and train generative AI and predictive models to capture disease heterogeneity, predict therapeutic responses, and identify novel targets and biomarkers.

• Contribute to the computational foundation of PDO avatar analytics, working closely with cell biologists, bioinformaticians, and clinical collaborators to extract meaningful biological insights.

• Provide assistance in partnership and commercial projects and contribute with scientific and computational insights useful for our partners.

• Present and communicate findings clearly to internal teams and external stakeholders; contribute to publications, presentations, and IP development.

Who You Are

• A driven computational scientist with deep curiosity for biology and a commitment to translational impact.

• A strong team player, eager to work in a fast-paced, cross-functional, and dynamic environment where continuous learning is encouraged.

• You’re proactive, rigorous, and comfortable taking ownership of large and complex problems end-to-end.

• You have an exceptional track record of delivering complex scientific projects at a sustained pace.

Must-have qualifications

• Ph.D. or Master’s degree + 4 years experience in machine learning, artificial intelligence, computational biology, computer science, or a related field.

• Extensive experience with Python, and deep learning frameworks such as PyTorch, JAX, or TensorFlow.

• Proven expertise in one or more ML domains: generative modeling, graph neural networks, language models, causal inference, multi-task/transfer learning, active learning, or diffusion models.

• Priior experience integrating multi-omics data into sophisticated AI models.

• Strong knowledge of biological data formats and omics analysis (especially RNA-seq, WGS).

• Familiarity with version control, cloud computing, and scalable model training (multi-GPU, distributed computing).

Nice-to-have qualifications

• Hands-on experience in oncology.

• Exposure to single-cell RNA-seq, spatial transcriptomics, or proteomics datasets.

• Prior contributions to open-source software and/or impactful publications in top-tier journals or ML conferences.

• Experience with pipeline development and workflow automation (e.g., Nextflow, Snakemake).

• Strong understanding of drug discovery workflows, target ID, and translational biology.