Senior ML Engineer, Computational Biology
il y a 1 semaine
At Orakl Oncology, we are accelerating the development of oncology treatments. Today, fewer than 5% of new cancer drugs succeed in clinical trials. Clearly, new methods are needed. We combine cutting-edge biology and AI to build the next generation of insight platforms with the world's largest cohort of patient tumor avatars. These avatars fuel our AI-powered predictive engine, helping to anticipate clinical trial outcomes, validate therapies, and uncover new drug candidates.
Our mission is simple yet ambitious: to bring more effective treatments to patients who need them - and to make drug development smarter, faster, and more personalized. We collaborate with top hospitals, research institutes, and pharmaceutical companies worldwide. Backed by leading investors, we are a fast-growing, mission-driven startup at the intersection of science and technology.
We are looking for a highly skilled technical profile who enjoys building new computational methods and turning them into reliable production tools. You will design, implement, and maintain computational biology methods based on multi-omics data. You will ensure that these methods are deployable on internal and external projects and robust at scale. You will work collaboratively with an interdisciplinary team of computational biologists, AI/ML experts, cell biologists, and oncologists to drive drug discovery in cancer.
This role is ideal for someone with an engineering background and a strong interest in biology, but any candidate who wants to build methods that scientists will use every day in production to advance cancer science is a strong fit.
Key ResponsibilitiesIn close collaboration with scientists, design and implement novel computational biology methods
Benchmark, validate, and compare alternative approaches; propose and test new algorithms or tools to improve performance, robustness, and scalability.
Translate research prototypes into production-ready pipelines with clear versioning and documentation. This will involve implementing and maintaining CI/CD workflows (testing, packaging, deployment, monitoring).
Collaborate with data engineering teams to run pipelines efficiently in our production environment (e.g. containers, workflow managers, cloud infrastructure).
Work closely with scientists and project teams to align on standards, architecture, and data models.
Collaborate with the experimental and data generation teams to understand data characteristics and operational constraints.
Document methods and pipelines so that non-developers can reliably use them (clear user guides, examples, release notes).
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