Machine Learning Engineer - AI Enablement
Role details
Job location
Tech stack
Job description
As a Machine Learning Engineer, you will implement the infrastructure that unlocks our scientific data and accelerates the discovery of life-changing treatments. You will work at the intersection of computational engineering and biology, partnering with scientists, ML experts, and DevOps engineers to put powerful AI tools directly into the hands of those driving drug discovery., * You build tools to evaluate AI/ML model performance and establish new ways to understand and measure AI quality.
- You partner with product managers and scientists to understand user needs, shape requirements, and translate them into actionable technical specifications.
- You develop and maintain data systems for collecting, structuring, and storing diverse scientific data that power advanced analytics, machine learning, and other data-driven initiatives.
- You implement, adopt, and evaluate new AI/ML algorithms and analytical techniques.
- You own features end to end from initial model evaluation through to production deployment, within a team that prizes technical rigor and mentorship.
- You shape how we build contributing to architectural decisions, code reviews, and the evolution of our development processes.
- You span the stack and contribute where needed, staying curious about emerging technologies and industry best practices.
Requirements
- You bring technical rigor with a Bachelor's, Master's or PhD in Computer Science or a related technical field, and proven experience in machine learning engineering. You treat testing, clean code, and documentation as the foundation for scalable, high-impact systems.
- You span the stack working across the ML lifecycle - from GPU optimization and model performance to backend systems and cloud-native architecture (e.g. Kubernetes, AWS) - with a solid grounding in statistics, ML theory, and modern AI/ML frameworks and tools.
- You value collaborative engineering , seeing code reviews and architectural decisions as spaces for shared growth, and translating complex technical challenges into shared goals with partners from research scientists to DevOps engineers.
- You are a continuous learner approaching new frameworks and languages with curiosity, as comfortable mentoring others as learning from them.
- You are an excellent communicator translating technical complexity into clear, actionable insights with empathy and precision, so every stakeholder feels aligned and heard.
Preferred:
- Experience with biological data (ideally multimodal) and scientific processes.
- Experience working with scientists or in a research environment.
- Experience with workflow automation, GenAI, and/or agents.
If you want to build the tools that make AI an everyday utility for scientists - and help bring life-changing medicines to patients faster - apply now and join us.