Member of Technical Staff, Applied AI

Who We Are

We are building frontier models that learn the fundamentals of biology. Our team includes researchers, machine learners, protein engineers, and biologists. We are committed to scientific excellence and fostering a culture of trust and interdisciplinary exchange.

Who You Are

• A strong ML researcher with experience in generative modelling.
• Experience in notable machine learning projects, such as open source contributions, product launches, or high-impact publications from venues like NeurIPS, ICML, ICLR, or Nature.
• A deep understanding of generative model architectures, training dynamics, and inference behavior.
• A skilled ML developer with experience writing robust, tested, and maintainable code.
• Experience building systems that serve large models via APIs and run inference on cloud hardware, parallelizing data and models across accelerators.
• A customer-facing and delivery-oriented individual who can translate complex technical concepts into clear language for both scientific and non-technical stakeholders.
• A passion for model performance, optimizing deep learning models for training and inference speed, ensuring deployments are performant, cost-effective, and reliable.
• Mission-driven and curious, motivated by the end goal and adaptable to different approaches and methodologies.
• Able to context-switch between deep technical work and customer-facing engagements.

What Sets You Apart (preferred, Not Required)

• Experience in computational biology or protein design.
• Experience building production enterprise software.
• A natural science background, academically trained in physics, biology, chemistry, or related fields.

Your Responsibilities

• Develop, deploy, and adapt our models for customer environments:
• 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, performance, and reliability.
• Customer partnership & product feedback:
• Work embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues, and deliver solutions.
• Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams.
• In collaboration with customer biology teams, plan and carry out model inference against biological targets. Quickly learn from results and feed insights back to our models.
• Gather and synthesise customer feedback, translating it into actionable insights for our product, research, and platform teams.
• Create technical documentation, integration guides, and best-practice resources.
• Spend time working on-site at international partner locations as needed.
• Self development:
• Stay on top of the latest developments in ML, model serving, and cloud-native tooling.
• Gain a strong working understanding of protein and cell biology.
• Collaborate in a joint codebase with other research scientists, engineers, and protein designers, maintaining highest code standards.
• Participate in knowledge sharing, organizing and presenting at our internal reading group.
• Attend and present at conferences.

What We Offer

• Competitive compensation and benefits packages, including private health insurance, pension contributions, generous leave policies (including gender-neutral parental leave), and travel opportunities.
• A stimulating work environment and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models.
• We welcome applicants from all backgrounds and are committed to building a diverse and inclusive team.