Member of Technical Staff, Applied AI

The role

You will develop, deploy, and adapt proprietary generative models for customers in the pharmaceutical and biotech sectors. You will work within an interdisciplinary team of machine learners, protein engineers, and biologists to translate model understanding into production systems.

What you will do

• Develop a deep working understanding of generative models architectures, training data, capabilities and limitations
• Collaborate in a joint codebase with research scientists, engineers and protein designers
• Drive end-to-end technical deployment of models into customer environments
• Design production-grade API integrations and model-serving infrastructure
• Adapt and fine-tune models to meet specific customer requirements
• Build ML data pipelines for customer-specific inference, evaluation and feedback workflows
• Ensure deployments meet customer standards for security, performance and reliability
• Work embedded with pharmaceutical and biotech partners to scope requirements and deliver solutions
• Serve as the technical point of contact for assigned customers
• Plan and carry out model inference against biological targets with customer biology teams

Required qualifications

• Strong ML researcher with experience in generative modelling
• Experience on notable machine learning projects documented by open source contributions, product launches or high impact publications at NeurIPS, ICML, ICLR or Nature venues
• Deep understanding of generative model architectures, training dynamics and inference behaviour
• Skilful ML developer who writes robust, tested and easy to maintain ML code
• Experience using version control and code review systems
• Experience building systems that serve large models via APIs
• Experience running inference on cloud hardware and parallelising data and models across accelerators
• Ability to translate complex technical concepts into clear language for scientific and non-technical stakeholders
• Detailed understanding of how ML libraries interplay with hardware and data
• Ability to optimise deep learning models for training and inference speed

Nice to have

• Experience in computational biology or protein design
• Experience building production enterprise software meeting security, compliance, auditability and uptime requirements
• Natural science background in physics, biology, chemistry or other related fields

Benefits

• Private health insurance
• Pension contributions
• Generous leave policies including gender neutral parental leave
• Hybrid working
• Travel opportunities

About the company

Latent Labs is building frontier models that learn the fundamentals of biology. The team includes members who co-developed DeepMind’s AlphaFold and invented latent diffusion.