Senior AI Research Scientist, Barcelona

Jordan martorell s.l.; Barcelona; Presencial; EUR 70.000 - 90.000; Jornada completa

Descripción de la vacante

A leading biomedical firm in Barcelona seeks a Senior AI Research Scientist to develop cutting-edge AI and machine learning technologies for drug discovery. The ideal candidate will have a PhD or MSc in a related field, strong programming skills, and experience in AI/ML applications in life sciences. This role emphasizes collaboration in a dynamic team setting, where creativity and innovation are crucial for advancing biomedical research and improving patient outcomes. Develop your career at the forefront of AI in science.

Formación

• PhD or MSc in relevant technical discipline with experience in AI/ML research.
• Hands-on ability to implement AI/ML techniques.
• Strong quantitative background in algebra, probability, calculus, and statistics.

Responsabilidades

• Deliver projects researching and developing AI methodologies.
• Design and conduct experiments for hypothesis testing.
• Collaborate with researchers to run wet-lab experiments.

Conocimientos

Fluency in English

AI/ML algorithm development

Hands-on programming in Python

Statistical analysis

Communication skills

Educación

PhD in machine learning, computer science, or related field

MSc with relevant experience

Herramientas

PyTorch

TensorFlow

NumPy

Pandas

Matplotlib

Descripción del empleo

Senior AI Research Scientist

This role is based in Barcelona, with an on‑site commitment of three days a week. Fluency in English is required.

Introduction to the opportunity

Are you passionate about creating artificial intelligence and machine learning systems for real‑world science applications? Does contributing to preventing, modifying, and even curing some of the world’s most complex diseases inspire you? Would you like to work on developing an iterative drug discovery and development process while drawing on methods across various fields, from active learning to optimisation and search? What about advancing our understanding of biology, streamlining research and development processes, and leveraging a variety of data modalities? Do you thrive working in a supportive, inclusive environment where creativity, collaboration across disciplines and lifelong learning towards innovative breakthroughs are encouraged? If yes, this opportunity may be for you.

Join our interdisciplinary Centre for Artificial Intelligence team working on the frontier of AI research for digital biology. Your work will support the next generation of medicines and vaccines at the intersection of AI, biology, and engineering. Your work will contribute to transforming the drug discovery and development value chain as we know it, uncovering novel biological insights, automating processes, streamlining decisions, and improving the overall pipeline across all therapeutic areas at AstraZeneca.

Accountabilities

You will work efficiently in a team to deliver projects optimally, researching, developing and using the novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. You will be part of multifunctional teams to conceive, design, develop and conduct experiments to test hypotheses, validate new approaches, and compare the effectiveness of different AI/ML systems, algorithms, methods and tools for new applications to support the discovery, design, and optimisation of medicines with improved biological activity. You will contribute to addressing challenges and opportunities in the drug discovery and development value chain processes and provide innovative solutions in fields such as deep learning, representation learning, reinforcement learning, meta‑learning, active learning approaches applied to de‑novo molecule design, protein engineering, in‑silico discovery, structural biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas. You will develop machine learning models designed explicitly for analysing heterogeneous biological data while collaborating with biology researchers to run algorithmically designed wet‑lab experiments to inform future experimental directions. You remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, and personal development initiatives and contributing to publications and academic and industry collaborations.

Essential Skills/Experience

A PhD in machine learning, statistics, computer science, mathematics, physics, or a related technical discipline, with relevant fundamental research experience in artificial intelligence and machine learning OR an MSc with a few years of relevant experience in the research and development of artificial intelligence and machine learning approaches to life sciences applications. Fundamental AI research and development experience with well‑rounded hands‑on ability to implement AI/ML techniques based on publications or developed entirely in‑house. In addition, experience in applying rigorous scientific methodology to (i) identify and create ML techniques and the required data to train models, (ii) develop machine learning model architectures and training algorithms, (iii) analyse and fine‑tune experimental results to inform future experimental directions, (iv) implement and scale training and inference engineering frameworks, and (v) validate hypotheses. Theoretical understanding, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, hands‑on experimentation, analysis, and AI/ML techniques visualisation. Algorithmic development and programming experience in Python or other programming languages and machine learning toolkits, especially deep learning (e.g. PyTorch, TensorFlow, etc.). Experience in practical aspects of AI/ML foundations and model design, such as improving experimentation and analysis of model efficiency, quantisation, conditional computation, reducing bias, or achieving explainability in complex models. Ability to communicate and collaborate effectively with diverse individuals and functions, reporting and presenting research findings and developments clearly and efficiently to other scientists, engineers and domain experts from different disciplines. Fundamental research with hands‑on practical experience and theoretical knowledge of at least one of the following research areas – examples include but are not limited to – multi‑agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non‑convex optimisation, Bayesian non‑parametric, natural language processing, approximate inference, control theory, meta‑learning, category theory, statistical mechanics, information theory, knowledge representation, unsupervised, supervised, semi‑supervised learning, computational complexity, search and optimisation, artificial neural networks, multi‑scale modelling, transfer learning, mathematical optimisation and simulation, planning and control modelling, time series foundation models, federated learning, game theory, statistical inference, pattern recognition, large language models, probability theory, probabilistic programming, Bayesian statistics, applied mathematics, multimodality, computational linguistics, representation learning, foundations of generative modelling, computational geometry and geometric methods, multi‑modal deep learning, information retrieval and/or related areas.

Desirable Skills/Experience

Experience designing new AI/ML approaches to deriving insights from proprietary and external datasets to generate testable hypotheses using algorithmic, mathematical, computational, and statistical methods combined with theoretical, empirical or experimental research sciences approaches. Fluent in Python, R, and/or Julia, other programming languages, including scientific packages and libraries (e.g. PyTorch, TensorFlow, Pandas, NumPy, Matplotlib). Research experience demonstrated by journal and conference publications in prestigious venues (with at least one publication as a leading author). Examples include but are not limited to NeurIPS, ICML, ICLR and JMLR. Practical ability to work on cloud computing environments like AWS, GCP, and Azure. Domain knowledge of tools, techniques, methods, software, and approaches in one or more areas, such as protein engineering, microbiology, structural biology, molecular design, biochemistry, genomics, genetics, bioinformatics, and molecular, cellular and tissue biology. Evidence of open‑source projects, patents, personal portfolios, products, peer‑reviewed publications, or similar track records.

Why AstraZeneca?

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life‑changing medicines. In‑person work gives us the platform we need to connect, work at pace, and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn’t mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Join the team, unlocking the power of what science can do. We are working towards treating, preventing, modifying, and even curing some of the world’s most complex diseases. Here, we have the potential to grow our pipeline and positively impact the lives of billions of patients around the world. We are committed to making a difference. We have built our business around our passion for science. Now, we are fusing data and technology with the latest scientific innovations to achieve the next wave of breakthroughs.

Ready to make a difference?

Apply now and join us in our mission to push the boundaries of science and deliver life‑changing medicines!

Python, PyTorch, TensorFlow, R, Pandas, NumPy, Matplotlib