Tech Lead

 Job description 

About Aqemia 

Aqemia is an in silico drug discovery start-up, whose ambition is to discover rapidly more innovative therapeutic molecules with better chances of success.​

How? Just like an AI can learn to play chess, Aqemia’s generative AI learns to invent relevant compounds thanks to unique Statistical Mechanics algorithms predicting drug-target affinity among other properties. 

Aqemia’s differentiation lies in its affinity prediction both accurate and 10 000x faster than competition, enabling efficient guidance of generation towards compounds with better chances to become drugs. 

Aqemia is a spin-off of the École Normale Supérieure Paris leveraging disruptive algorithms from 8 years of research. Aqemia’s team is composed of high profiles at the crossroads of Medicinal Chemistry, Statistical Mechanics and Artificial Intelligence.


We’re looking for a Tech Lead to join our core team and make an impact on a critical challenge: discovering drug candidates to cure key diseases. You will work in an interdisciplinary team of drug hunters, physicists, chemists and ML engineers.

Founded in June 2019, we have raised €1.6M with leading VC fund Elaia Partners and Bpifrance.

If this sounds exciting to you, come and join us! 

Job description

As a Tech Lead, you will collaborate with ML researchers and engineers to develop and accelerate experiments exploring new applications of AI and Statistical Mechanics to Drug Discovery. 


Your day-to-day responsibilities:

  • Architect, manage the software and compute infrastructure

  • Implement tools, libraries and frameworks to speed up research and development

  • Enforce software engineering best practices and mentor the research teams

  • Design cloud infrastructure to serve millions of physics-based calculations and ML predictions

  • Write continuous integration and delivery tools to build new Docker containers, deploy updated models, and distribute code in response to Git hooks or other web events

  • Connect Docker-based microservices and serverless scripts to enable automated dataset ingestion pipelines that speed up the pace of model development and serving

  • Architect and build cloud-based data lakes along with data APIs to power machine learning models, visualization tools, and chemistry software

  • Optimise codebases to speed-up the screening runtime

  • Implement security policies across Aqemia’s software infrastructure

Preferred experience

Your profile:

  • Bachelor’s or Master’s degree in computer science (or equivalent experience)

  • Experience in using Docker and Kubernetes to containerize and launch microservices. ML-specific experience not required

  • Experience and appetite for building production systems in Python, especially in a microservices or serverless environment

  • Strong expertise with either AWS or GCP, including maintaining a VPC, writing build tools, tests and CI workflows and enforcing best practices for authorization and authentication


Nice to have:

  • Basic knowledge in physics, biology, chemistry or machine learning

  • Experience using TensorFlow, Jax, NumPy, Pandas or similar ML/scientific libraries


You should join us if…

  • You are passionate about solving difficult problems on topics that really matter

  • You are curious, willingful and dynamic

  • You want to grow and help others grow as well

  • You like working collaboratively in an interdisciplinary, fast-paced environment

  • You believe in silico/AI can have strong impact on how to find new drugs

  • You want to join a small team to bring your own impact in drug discovery


To apply, send us your CV:

Aqemia is growing fast. You do not fit in this job description but are excited by this adventure?