Head of AIStraight from AQEMIA’s careers page. Apply on the company site — no recruiter, no middleman.
Head of AI
Team: Platform
Location: Paris
Commitment: Full-time contract
Workplace Type: hybrid
About AQEMIA
About our Team
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 AQEMIAs 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.
The role
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 high‑throughput workflows that power our discovery platform. This is a strategic and people‑focused 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 platform‑level systems design, guiding the development of architectures capable of understanding complex molecular environments and making high‑impact predictions. Your work blends state-of-the art ML thinking with leadership, decision‑making, and cross‑functional 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 high‑performance 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 high‑throughput ML workflows by overseeing the design of production‑ready 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 decision‑making.
- 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.
Qualifications
- 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, high‑dimensional data - experience with SOTA architectures and research‑driven 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 production‑adjacent environments.
Nice‑to‑Have
- 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 large‑scale scientific pipelines.
- Background in platform or infrastructure‑level ML development.
- Contributions to research communities (papers, open‑source, benchmarks)
Why Join Us?
Similar remote jobs
More like this →
UP.Labs
Head of Engineering, AI Logistics Billing Platform
Discover More than 100,000 Hidden Remote Jobs Before Everyone Else
Unlock All Remote Jobs Today
Simple pricing. Big savings on Quarterly and Yearly.
Monthly Access
- Instant access to fresh remote jobs from 500+ companies
- New opportunities added hourly, often 3-7 days before anywhere else
- Advanced filtering by role type, stack, pay, and location
- Priority customer support
Yearly Access
- Everything in Monthly
- Save $169 (~74%) vs paying monthly
- Average job search takes ~6 months - get covered for the whole journey
- Less than the cost of one lunch per month for competitive advantage
- Equivalent to just ~$4.92/month