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.
Design and deploy secure, scalable cloud infrastructure for AI and machine learning workflows using AWS and Azure. Enable data scientists and ML engineers through infrastructure-as-code, reusable libraries, and best practices. Work cross-functionally to solve complex technical challenges in life sciences and other sectors.
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 the development and strategy of a unified data platform integrating wet and dry lab data to support drug discovery. Manage data engineering teams, optimize scientific compute pipelines using Nextflow and Seqera, and ensure FAIR data principles with strong governance and lineage. Operate in a highly interdisciplinary environment bridging biology, machine learning, and engineering.
This role involves leading the design of production-grade machine learning and GenAI solutions on the Databricks platform, with a focus on MLOps, RAG architectures, and LLM deployment. The engineer will act as a technical advisor during sales cycles, build MVPs, and support customers in implementing AI workflows. Responsibilities also include creating technical content, mentoring teams, and driving platform adoption through thought leadership and community engagement.
This role involves managing key enterprise retail accounts and driving the adoption of AI solutions across customer experience, supply chain, and operations. The Account Director will act as a strategic advisor, working closely with technical teams to design and execute complex deployments while shaping product strategy through market insights. Success requires deep industry expertise, executive engagement, and a long-term vision for AI transformation in the retail sector.
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 leading a team of ML engineers while maintaining hands-on technical contributions to production-grade machine learning systems. The engineer will shape ML best practices, guide solution design, mentor junior staff, and lead cross-functional teams through the full lifecycle of AI/ML projects—from discovery to deployment and continuous improvement. A strong emphasis is placed on technical leadership, code quality, and clear communication with non-technical stakeholders.
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.
