Head Of ai H/F

Aqemia
Paris, France
about 1 month ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours

Tech stack

Artificial Intelligence Python (Programming Language) Machine Learning Molecular Modeling Information Technology Machine Learning Operations

Job description

AQEMIA is building one of the most ambitious applications of AI with physics: predictive systems that can evaluate and design drug candidates at massive scale. As Head of AI, you will lead the team responsible for the foundational models and highthroughput workflows that power our discovery platform. This is a strategic and peoplefocused role: you will manage and mentor a 10-15 people ML research team, define the roadmap, set priorities, and ensure execution across research, engineering, and scientific stakeholders.

You’ll operate at the intersection of frontier machine learning, scientific modeling, and platformlevel systems design, guiding the development of architectures capable of understanding complex molecular environments and making highimpact predictions. Your work blends state-of-the art ML thinking with leadership, decisionmaking, and crossfunctional alignment. Within 12 months, success means delivering breakthroughs in predictive accuracy, throughput, and reliability, enabling AQEMIA to accelerate discovery cycles and push the boundaries of scientific AI.

Responsibilities

  • Lead AQEMIA’s ML research organisation by managing, mentoring, and growing a ~15 person team of ML scientists and researchers, fostering autonomy, scientific excellence, and highperformance execution.
  • Own the predictive ML modeling roadmap by defining priorities, set direction, and align research initiatives with platform evolution and company strategy.
  • Drive frontier ML innovation, steer exploration of advanced architectures (transformers, GNNs, diffusion models, multimodal systems) and emerging techniques relevant to scientific AI.
  • Scale highthroughput ML workflows by overseeing the design of productionready pipelines capable of evaluating massive chemical spaces efficiently and reproducibly.
  • Collaborate across disciplines partnering with chemistry, biology, physics, engineering and leadership teams to integrate ML insights into discovery workflows and strategic decisionmaking.
  • Ensure scientific rigor and model excellence, establish standards for benchmarking, validation, and robustness across all predictive systems.
  • Translate research into platform impact ensuring modeling breakthroughs directly accelerate AQEMIA’s drug design cycles and unlock new capabilities for the platform., At AQEMIA, we work for a mission: joining us means having your own impact on changing the way drugs are discovered, and helping to shape the direction of our fast-growing company and team.Expanding Drug Discovery Pipeline: Focused on critical therapeutic areas like Oncology, CNS, Immuno-inflammation… with in vivo proof of concept/patent stage programs. Collaborations with top Pharma, including a $140M Sanofi deal.World-Class Interdisciplinary Team: work alongside exceptional talent at the intersection of technology and life sciences. Our teams combine deep expertise in AI, physics-based modeling, biology, and medicinal chemistry to push the boundaries of innovation.DeepTech Recognition: AQEMIA is proud to be part of theFrench Tech 120andFrance 2030, highlighting our role as a key player in Europe’s DeepTech ecosystem.Prime Location with Flexibility: Our offices are located in the heart ofParisandLondon (King’s Cross), with flexible work arrangements including up to two remote days per week.Strong Financial Backing: $100M raised from leading European and International investorsWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Requirements

Master’s degree or PhD in ML/AI or a computational scientific field (machine learning, computer science, physics, applied mathematics).

  • 5+ years leading scientific or technical teams, including mentoring, hiring, performance management and roadmap ownership.
  • Deep expertise in machine learning for complex, highdimensional data - experience with SOTA architectures and researchdriven model development.
  • Strong Python and scientific ML ecosystem experience.
  • Experience building predictive models for scientific or structured domains (drug discovery, biology, physics, materials, climate, etc.).
  • Ability to evaluate and improve model performance, efficiency, and robustness in productionadjacent environments., Experience in drug discovery, computational chemistry, or molecular modeling - domain knowledge is a plus but not required.
  • Familiarity with multimodal ML (e.g., combining structural, sequence, and chemical data).
  • Experience scaling ML systems across multiple programs or largescale scientific pipelines.
  • Background in platform or infrastructurelevel ML development.
  • Contributions to research communities (papers, opensource, benchmarks)

About the company

About AQEMIA AQEMIA is a drug invention company dedicated to creating entirely new medicines to address major unmet medical needs.At the core of our mission isQEMI, our proprietary molecule-invention platform, which uniquely combines cutting-edge science with advanced technology. Powered by physics-based modeling, statistical mechanics, and generative AI, QEMI allows our teams to design novel drug candidates from first principles.What makes AQEMIA different is our commitment to true innovation: our research is dedicated to the invention of new molecular entities, not the refinement of existing ones. We focus on inventingnever-before-seen molecules, without relying on experimental data, and advancing them into a growing pipeline of proprietary programs and strategic partnerships with leading pharmaceutical companies.Our most advanced preclinical programs are currentlyin vivo optimization, targeting diseases still waiting for effective treatments, offering our teams the opportunity to work on science that can make a real difference in people’s lives.For more information, visitour, and our. About our Team AQEMIA brings together a diverse, multidisciplinary team of 80+ professionals based in Paris and London. Our scientists and engineers, including chemists, physicists, machine learning experts, and software engineers, work side by side to push the boundaries of early-stage drug discovery.This close collaboration across disciplines is central to our approach, enabling us to tackle complex scientific challenges from first principles and translate cutting-edge ideas into novel therapeutic candidates. At AQEMIA, team members are encouraged to contribute their expertise, learn from one another, and play an active role in shaping the future of drug invention. About our Platform Department

The Platform team (~20 people) brings together multidisciplinary teams working on the scientific core of Aqemia’s drug discovery engine. Its mission is to build scalable and reproducible workflows enabling multiple drug discovery programs to run in parallel with minimal manual intervention.

The team combines expertise across Artificial Intelligence and Machine Learning (both research and applications), data science, statistical physics and molecular simulations, computational chemistry (CADD), and other scientific disciplines. Together, they develop predictive models, physics-based simulations, and robust scientific pipelines that power AQEMIA’s discovery platform.

At the center of this ecosystem is the Rocket Launcher process: an industrialized workflow continuously launching, testing, and improving drug discovery projects through iterative scientific feedback loops.

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