Senior Scientific Machine Learning Engineer

Scientific Machine Learning Engineer

Barcelona, Spain

An exciting opportunity has arisen to join a cutting-edge biotech start-up at the forefront of AI-driven biology. We’re seeking a Scientific Machine Learning Engineer to take ownership of building advanced modelling systems that help scientists understand and influence cellular behaviour. This is your chance to work on technically ambitious challenges, with a focus on sparse, noisy, high-dimensional biological data, and create tools that will revolutionize therapeutic development.

The Employer

Our client is a rapidly growing, venture-backed biotech innovator developing an AI-native platform designed to bridge experimental data, biological knowledge, and machine learning models. By uniting computational power with scientific insight, they’re enabling breakthroughs in cell therapy, manufacturing, and biomedicine. Based in Barcelona, this is an early-stage environment where technical depth, curiosity, and collaboration drive everything they do. They support relocation, offer significant early equity, and give you the opportunity to shape the technical foundations from day one.

Qualifications & Experience

• Background in applied mathematics, machine learning, computational physics, statistics, or related discipline.
• PhD-level research OR comparable depth through hands-on engineering experience in modelling complex real-world systems.
• Proven experience building models from scratch, with strong judgment on data, uncertainty, and validation.

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Responsibilities

In this founding role, you’ll design and implement models that become core to the platform, including:

• Develop novel modelling systems for sparse and noisy biological data.
• Incorporate biological knowledge into ML models to produce predictive, interpretable insights.
• Quantify uncertainty, guide experiments, and inform scientific decision-making.
• Help define infrastructure, technical approaches, and validation culture.

Skills / Technical Competencies

• Machine learning for real-world systems.
• Expertise in probabilistic modelling, Bayesian methods, causal inference, or dynamical systems.
• Strong programming skills in Python or similar languages for scientific computing.

Nice to Haves

• Experience with mechanistic or hybrid ML models.
• Background in optimal experimental design or scientific ML.
• Track record in research-to-production environments or deep-tech start-ups.

This is a unique opportunity to work on mission-driven, highly technical challenges in a collaborative, interdisciplinary setting. If you’re passionate about building AI systems that impact the future of science, we’d love to speak with you.