Member of Technical Staff, Applied AI

The opportunity

We are looking for a Member of Technical Staff with deep expertise in generative modelling to work at the interface between our frontier models and the customers who depend on them. You will join an interdisciplinary team of machine learners, protein engineers and biologists, jointly working to change the way that we control biology and cure diseases. In your role you will develop an in-depth understanding of our proprietary generative models and apply that knowledge to deploy, adapt and optimise them within customer environments - particularly in the pharmaceutical and biotech sectors.

This is a hybrid role. You will need a researcher’s depth of understanding of our models, combined with the pragmatism and communication skills to translate that understanding into production systems that deliver scientific value for our partners.

• You are a strong ML researcher with experience in generative modelling. You have worked on notable machine learning projects, as documented by your contributions to widely used open source libraries, significant product launches or high impact publications, e.g. at NeurIPS, ICML, ICLR or Nature venues. You have a deep understanding of generative model architectures, training dynamics and inference behaviour.
• You are a skilful ML developer. You write ML code that is robust, tested and easy to maintain. You have experience using version control and code review systems. You are a fast prototyper and hacker who can also write beautiful production code. You have experience building systems that serve large models via APIs and running inference on cloud hardware, parallelising data and models across accelerators.
• You are customer-facing and delivery-oriented. You thrive in environments where customer success is the primary measure of your work. You can translate complex technical concepts into clear language for scientific and non-technical stakeholders alike.
• You are passionate about model performance. You have a detailed understanding of how ML libraries interplay with hardware and data and love to optimise deep learning models for training and inference speed. You use this knowledge to ensure that customer deployments are performant, cost-effective and reliable.
• You are mission driven and curious. You are passionate about making a positive impact on the world, whether it’s for patients, customers or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies. You are curious about problems, however small or big they appear. You thrive in a dynamic environment where you must context-switch between deep technical work and customer-facing engagements.

• You have experience in computational biology or protein design. You have worked on ML-driven projects in biology and understand the unique data challenges, evaluation paradigms and scientific workflows of biological modelling.
• You have built production enterprise software. You have experience delivering software that meets enterprise-grade requirements for security, compliance, auditability and uptime.
• You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields, giving you an intuitive understanding of the scientific problems our customers are solving.

• Develop a deep working understanding of our generative models - their architectures, training data, capabilities and limitations.
• Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards.
• Drive the end-to-end technical deployment of Latent Labs models into customer environments, designing production-grade API integrations and model-serving infrastructure.
• Adapt and fine-tune models to meet specific customer requirements, collaborating closely with our research team to ensure scientific rigour.
• Build ML data pipelines for customer-specific inference, evaluation and feedback workflows.
• Ensure deployments meet customer standards for security,…