Lead ML Engineer
The Lead ML Engineer joins a technical team focused on developing advanced machine learning architectures for health-related applications. The role supports the organisation's mission to harness innovative AI solutions within a research-driven environment.
About the Company
[Limited data provided — review with hiring manager before publishing].
Role Overview
The Lead ML Engineer is responsible for designing and developing complex ML systems, including multimodal encoders, temporal sequence models, and causal inference modules. The position involves defining the research roadmap, making strategic infrastructure decisions, and leading a team of ML specialists. Success is measured by delivering robust, validated models that process diverse datasets such as clinical and wearable health signals. The role also includes representing the technical vision to stakeholders and mentoring future team members.
Key Skills & Experience
- Lead the design and delivery of novel deep learning architectures, including multimodal and temporal models.
- Manage the development of probabilistic ML frameworks with uncertainty quantification.
- Build and optimise ML infrastructure, including training systems and model validation pipelines.
- Demonstrate experience with models used on real-world health datasets, including clinical and biological data.
- Take models from research prototypes through validation on datasets suitable for deployment.
- Maintain a research mindset balancing scientific rigor and product needs.
Nice to Have
[Limited data provided — review with hiring manager before publishing].
Key Responsibilities
- Lead the development of ML architectures and define research directions.
- Build the core ML engine processing diverse health datasets.
- Make strategic infrastructure decisions, balancing build versus buy options.
- Establish best practices for model development, deployment, and validation.
- Mentor team members and provide technical leadership.
- Communicate the organisation's AI capabilities to stakeholders, investors, and partners.
Requirements
- Proven experience designing and delivering robust deep learning architectures.
- Strong expertise in sequence, multimodal, and representation learning.
- Deep understanding of probabilistic machine learning, uncertainty, and model calibration.
- Experience with causal inference or related approaches is highly desirable.
- Comfortable working with ML infrastructure including training systems and frameworks.
- Ability to transition models from prototypes to validated real-world applications.
- Right to work in the specified location.
- Location: On-Site alternate with hybrid working environment.
- Employment Type: Asia Permanent.
- Duration: 2 weeks starting from 22/07/2026.
If you have the relevant skills and experience, please apply with an updated CV.
