Operations Engineering Intern

il y a 3 jours

Rue Gabriel Péri Le KremlinBicêtre France Orakl Oncology Temps plein

Orakl's develops a rapidly scaling operational platform that cultures, expands, freezes, and screens hundreds of patient-derived organoids (PDOs). As a Platform Engineering Intern, you will work hand-in-hand with the Orakl Oncology CTO on problems that sit at the heart of the company's execution: designing, modeling, and optimizing the systems that govern how organoids move through the organization.

You will take ownership of analyzing Orakl's end-to-end organoid operations as a production system—identifying bottlenecks, defining performance metrics, and translating biological workflows into quantitative, optimizable processes. Your work will directly inform how capacity is planned, how resources are allocated, and how the platform scales reliably as demand increases.

You will address high-impact operational questions with real constraints: how to optimize duplication and expansion strategies under limited lab capacity; how to model the optimal number of organoid lines to thaw, expand, or freeze at any given time; how to integrate stochastic arrivals of fresh patient samples into deterministic planning; how to increase weekly screening throughput without sacrificing data quality; and how to maintain rigorous traceability while minimizing operational overhead. You will collaborate closely with wet-lab operators, operations planners, and data scientists, turning analysis into deployable process changes.

Your Missions

  • Partner directly with the CTO to map, formalize, and redesign Orakl's core operational workflows (sample intake, organoid generation, duplication cycles, freezing, and drug-screening pipelines).

  • Build quantitative models and analyses to evaluate throughput, capacity, utilization, and trade-offs across the organoid platform.

  • Identify bottlenecks and failure modes, and propose concrete operational strategies to improve scalability, robustness, and efficiency.

  • Prototype, test, and iterate on workflow and planning improvements in close collaboration with laboratory and operations teams.

  • Communicate results clearly and rigorously to scientific, operational, and leadership stakeholders, supporting data-driven decisions at the company level.

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