全职 Lead AI Machine Learning Scientist - Healthtech 工作, 薪水, Gravitas Recruitment Group (Global) Ltd 公司招聘中 - Ricebowl

Key Responsibilities

• Lead the design and development of advanced ML architectures, including multimodal encoders, cross-modal fusion systems, temporal sequence models, uncertainty quantification frameworks, and causal inference modules.
• Define and execute the research roadmap for synthetic-to-real data transfer, model validation, and real-world performance evaluation.
• Build the core ML engine that processes and learns from diverse datasets, including psychological assessments, clinical data (e.g., blood work and DNA), and wearable-derived health signals.
• Make strategic decisions on ML infrastructure, including evaluating build-versus-buy choices and determining when custom solutions are required versus existing frameworks.
• Establish best practices for model development, experimentation, deployment, and scientific validation.
• Hire, mentor, and provide technical leadership to future ML and AI team members.
• Represent the technical vision and capabilities of the AI platform to investors, scientific advisors, partners, and future engineering hires.

Must-Have Qualifications

• Proven experience designing and delivering novel deep learning architectures, beyond fine-tuning existing models.
• Strong expertise in sequence/temporal modeling, multimodal learning, and representation learning.
• Deep understanding of probabilistic machine learning, including uncertainty quantification, model calibration, and reliability.
• Experience with causal inference, counterfactual modeling, or related approaches is highly desirable.
• Comfortable working at a foundational level of ML systems, including experience with training infrastructure, optimization, autodiff systems, or ML framework internals.
• Demonstrated ability to take models from early research prototypes or synthetic datasets through validation on real-world data.
• Strong research mindset with the ability to balance scientific rigor and practical product development.

Nice-to-Have Qualifications

• Experience working with healthcare, biology, aging science, clinical datasets, or other complex longitudinal data.
• Previous experience as a first ML hire, founding engineer, or technical leader in an early-stage startup.
• Strong publication record in relevant machine learning, AI, or computational science venues.
• Open-source contributions to ML frameworks, research tools, or related technologies.
• Experience building AI systems in regulated or high-impact domains.

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