> Markdown version of [/jobs/ext/2020553-founding-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2020553-founding-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Machine Learning Engineer - **Company:** Tolemy Biology - **Location:** Barcelona, Spain - **Salary:** €60,000.0 - €100,000.0 - **Contract:** Permanent contract - **Skills:** Bioinformatics, Computational Biology, Dynamical Systems, Experimental Data, Machine Learning, Open Source Technology, Scientific Computating, Build Tools - **Published:** August 11, 2026 - **Apply:** https://es.trabajo.org/oferta-5001-178c435fc28305ec5d4eadd060c2778f ## About the Role from sparse biology. Biological data is noisy, expensive, high-dimensional, and incomplete. How do we learn useful representations of cellular state from limited experimental data? Building models scientists can trust. Cells contain real biological structure: metabolism, regulation, signalling, transport, and stress responses. How do we combine this knowledge with ML to build models that are predictive and biologically meaningful? Guiding better experiments. Useful models should help scientists understand uncertainty, compare hypotheses, and decide what to test next. How do we evaluate models when there is no clean benchmark for "understanding a cell"? What We're Looking For We're looking for someone with strong machine learning judgment and experience building models for difficult real-world systems. You should be able to reason from first principles about data, models, compute, uncertainty, validation, and product usefulness. You should also enjoy ambiguity, care about scientific truth, and want to build systems that help users make better decisions. We care more about depth, judgment, and evidence of exceptional work than credentials. You may have PhD-level training in machine learning, physics, biology, chemistry, applied mathematics, computational biology, or another systems-oriented discipline. You should have strong foundations in one or more of: applied mathematics, statistics, optimisation, probabilistic modelling, causal inference, dynamical systems, scientific ML, or related areas. You may also be an exceptional applied ML engineer without a PhD, with a track record of building models for complex real-world systems. Experience with biological data is useful, but not required. What matters most is comfort with natural-world systems: messy, noisy, sparse, nonlinear, and only partially observed. Nice to Have - Experience with mechanistic models, hybrid ML, Bayesian methods, causal inference, dynamical systems, or uncertainty quantification. - Experience designing benchmarks in novel or poorly defined problem spaces. - Experience building models that move from research into production or user-facing workflows. - Publications or open-source work in ML, computational biology, scientific computing, or related areas. - Experience in a high-growth company, deep tech startup, or research-to-production environment. Job Type: Full-time Pay: 60,000.00€ - 100,000.00€ per year Work Location: In person ## Description our technical approach, infrastructure, and scientific validation culture from the beginning. - A deeply interdisciplinary team. You'll work directly with a team across cell biology, systems biology, software, product, and machine learning. Life at Tolemy We're building an in-person team in Barcelona, working together in English. We support relocation and visa sponsorship where needed. Our values are simple: stay curious, be kind always, and move with purpose. We care about technical depth, but also about how people work together, learn, communicate, and support each other. We offer competitive salary, meaningful early-employee equity, flexible time off, flexible work-from-home arrangements, a learning and conference budget, high-quality equipment, and practical support to help you make Barcelona home if it's not already. Hard Technical Challenges Orbit sits at the edge of machine learning, biology, and experimental science. Some of the hardest problems you'll work on include: Learning ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london)