Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate directly with customers and technical teams to prototype, deploy, and iterate on high-impact tools across aerospace, energy, and advanced manufacturing. Work across backend services, APIs, and cloud infrastructure while contributing to core platform development.
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate with data scientists and customers to develop, prototype, and deploy production-grade tools using Python, FastAPI, and cloud infrastructure. Work across industries such as aerospace, energy, and semiconductors, with opportunities for international travel to support on-site customer deployments and iterate on solutions based on direct user feedback.
This role involves building and deploying production-grade machine learning systems for high-impact clients in national security and AI safety. The engineer will lead technical architecture decisions, operationalise models using frameworks like TensorFlow and PyTorch, and develop scalable ML infrastructure. Collaboration with cross-functional teams and direct client engagement are key, with a focus on translating complex AI concepts into real-world solutions.
Lead or contribute to drug discovery projects by integrating medicinal chemistry expertise with AI-driven platforms. Collaborate closely with machine learning teams to refine predictive models and drive programs from hit identification to candidate selection. Play a key role in shaping next-generation drug design through innovation in structure-based and fragment-based approaches, while mentoring teams and managing external research partners.
Lead and develop the platform engineering team responsible for hybrid infrastructure, Kubernetes-based clusters, and scientific compute environments that support machine learning and drug discovery workflows. Define platform strategy, ensure robust CI/CD, observability, and security, and act as a technical leader across engineering and data science. The role involves shaping a developer platform tailored to computational scientists and genomic data pipelines in a high-impact TechBio environment.
This role involves strategic talent acquisition for OpenAI's AI/ML research teams across EMEA, focusing on identifying and engaging leading researchers and technical scientists. The recruiter will work closely with research staff to shape hiring strategies, anticipate talent needs, and guide decisions impacting frontier AI research. It emphasizes deep technical understanding, proactive talent cultivation, and advisory input into candidate evaluation and compensation in a fast-evolving domain.
Develop and optimise cutting-edge AI models at the intersection of machine learning and drug discovery, translating research into scalable, production-ready systems. Work closely with scientists to build predictive and generative models using advanced deep learning techniques, with a focus on innovation in computational biology and chemistry. Contribute to a collaborative, interdisciplinary team advancing a digital biology platform for transformative medicine development.
Lead the product vision and strategy for Wayve’s simulation, evaluation, and validation platforms used to develop and test AI-driven autonomous vehicles. Guide cross-functional platform teams to build unified, high-performance developer tools that ensure safety, reliability, and rapid iteration. The role involves deep collaboration with engineering and science teams, setting quality standards, and shaping how AI systems are validated before real-world deployment.
This role involves developing scalable software systems to support virtual drug discovery, focusing on integrating bio foundation models into production workflows. The developer will build and optimize pipelines for biological data processing, create tools for data visualization and quality control, and collaborate with ML scientists and engineers. The position sits at the intersection of computational biology and software engineering within a high-growth startup environment.
This role involves developing and prototyping generative machine learning models for protein design, with a strong focus on real-world biological applications. You'll work closely with biologists and engineers to build scalable data pipelines, optimize model performance, and iterate based on wet lab feedback. The position emphasizes research excellence, production-grade code, and rapid experimentation in a cross-disciplinary environment.
This role involves deploying and integrating generative AI models into pharmaceutical and biotech customers' scientific workflows. The engineer will design production-grade API integrations, data pipelines, and model-serving infrastructure while working on-site to troubleshoot and deliver solutions. The position emphasizes customer collaboration, enterprise-grade deployment standards, and translating real-world feedback into product improvements.
This role involves working directly with client teams to design, build, and deploy AI-driven solutions in live research environments. You will translate complex research questions into production-ready workflows, optimize LLM applications, and integrate data pipelines across cloud platforms. The role also includes collaborating with non-technical stakeholders and feeding learnings back into the product and engineering teams.
This role involves analyzing and synthesizing internal and external healthcare data to generate actionable medical insights that inform strategic decision-making in the HIV therapeutic area. The individual will support integrated insights workflows, contribute to AI-enabled analytics initiatives, and collaborate with cross-functional teams to enhance evidence-based planning. Emphasis is placed on data governance, operational excellence, and innovation in insights generation.
Design and build scalable web applications and API services for a biomedical knowledge platform using Node.js, TypeScript, and modern fullstack technologies. Collaborate with product and research teams to deliver features across the development lifecycle. Work with graph databases, full-text search, and cloud infrastructure in a high-impact AI environment.
Design and build scalable web applications and API services for a high-precision biomedical knowledge platform using Node.js, TypeScript, and GraphQL. Collaborate with product and research teams to deliver features across the full development lifecycle. Work with technologies including Neo4j, ElasticSearch, and AWS to support AI-driven scientific discovery.
