Forward Deployed AI Engineer

Role Overview

• Drive the end-to-end technical deployment of Latent Labs models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure.
• Design and build production-grade API integrations, data pipelines and model-serving infrastructure tailored to each customer’s requirements.
• Work on-site or embedded with pharma and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions.
• Ensure deployments meet enterprise standards for security, performance and reliability.
• Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including spending time working on-site at international partner locations as needed
• Gather and synthesise customer feedback, translating it into actionable insights for our product, research and platform teams.
• Collaborate with internal teams to shape the product roadmap based on real-world deployment learnings.
• Create technical documentation, integration guides and best-practice resources for customers.
• Stay on top of the latest developments in ML infrastructure, model serving and cloud-native tooling.
• Gain a strong working understanding of protein and cell biology as it relates to our product.
• Participate in knowledge sharing, e.g. organise and present at our internal reading group.

Requirements

• You have a strong CS or ML educational background. You hold a degree (BSc, MSc or PhD) in Computer Science, Machine Learning, or a closely related quantitative field. You have a solid grounding in software engineering principles and modern ML frameworks.
• You have built systems that access large models via APIs. You have significant experience designing, deploying and maintaining infrastructure for large-scale model serving and have hands-on experience building robust API layers around ML models.
• You are customer-facing and delivery-oriented. You have direct experience deploying AI systems for external customers. You can translate complex technical concepts into clear language for non-technical stakeholders and thrive in environments where customer success is the primary measure of your work.
• You are fluent in cloud infrastructure. You have hands-on experience with AWS and ideally other major cloud platforms (GCP, Azure). You are comfortable with containerisation (Docker, Kubernetes), CI/CD pipelines, and cloud-native architectures.
• You are a strong communicator and collaborator. You work effectively across functions
• with research scientists and business executives alike. You are comfortable leading technical discussions, writing clear documentation, and presenting solutions to senior stakeholders at partner organisations.
• You are mission driven and adaptable. You are passionate about making a positive impact on the world, whether it’s for patients, customers or beyond. You thrive in a dynamic, fast-paced environment where priorities can shift and you need to context-switch between multiple customer engagements.
• You have experience with bio or protein design models. You have worked on ML-driven projects in computational biology, protein design, or related life science domains. You understand the unique data challenges and evaluation paradigms of biological modelling.
• You have contributed to generative modelling innovation. You have a track record of novel contributions to generative modelling
• whether through publications, open-source work, or impactful product features.
• You have built production enterprise software. You have experience delivering software that meets enterprise-grade requirements for security, compliance, auditability and uptime. You understand the difference between a prototype and a production system.
• You have pharma or biotech industry experience. You understand the regulatory landscape, data governance requirements and scientific workflows common in pharmaceutical and biotech organisations.

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes

Benefits

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