Description :

We are seeking an AI R&D Lead to lead a multidisciplinary team building LLM and multimodal AI systems for clinical use within telemedicine platform. This is a techno-functional leadership role requiring deep expertise in modern AI systems alongside strong grounding in clinical safety, regulatory frameworks, and Software as a Medical Device (SaMD).

The role combines hands-on technical contribution with team leadership, ensuring that AI systems are not only effective, but clinically safe, auditable, and deployable in real-world health systems. This role is for an intrapreneurial interdisciplinary thinker who is excited at innovating and translating advances in generative AI to products that benefit vulnerable communities.

Essential Duties and Responsibilities:

1. Technical and Research Leadership

- Define and execute the roadmap for LLM and multimodal AI across the organization

- Make architectural decisions across prompting, fine-tuning, retrieval (RAG), and multimodal system design

- Set standards for model evaluation, safety, and deployment

2. Team Leadership and Management

- Build, manage, and mentor a team of AI/ML researchers, engineers, clinicians

- Establish clear goals, execution plans, and performance expectations for the team

- Create a culture of rigor, documentation, and accountability-especially around clinical risk

- Balance research exploration with product delivery timelines

3. Clinical Safety and Risk Oversight

- Own safety frameworks for AI systems, including hallucination mitigation and fail-safe design

- Ensure human-in-the-loop systems are appropriately designed and implemented

- Oversee structured risk assessments and alignment with clinical protocols

4. Regulatory and SaMD Alignment

- Drive alignment of AI systems with SaMD principles and regulatory expectations

- Ensure documentation, traceability, and auditability across the model lifecycle

- Work with leadership and partners on regulatory strategy and compliance pathways

5. Multimodal and LLM System Development

- Guide development of systems combining text, voice, image, and structured data

- Oversee adaptation of foundation models for low-resource, high-variability environments

- Ensure systems are robust to real-world data quality issues

6. Cross-functional Execution

- Partner with product, engineering, and clinical teams to translate research into deployable features

- Act as the primary interface between AI, clinical stakeholders, and external partners

- Support fundraising and strategic partnerships with clear articulation of AI capabilities and safeguards

7. Innovation at the last mile

- Design and deploy LLM and multimodal AI systems for low-resource settings, accounting for constraints such as limited connectivity, low-spec devices, and variable data quality

- Ensure robustness to missing or noisy inputs and optimize for offline-first or low-bandwidth environments

- Adapt systems for usability by frontline health workers with varying levels of clinical training and digital literacy

- Incorporate contextual factors (e.g., health system fragmentation, population diversity, and infrastructure variability) into model design, evaluation, and deployment

Qualifications:

Advanced degree (PhD, MD, or equivalent experience) in machine learning, biomedical engineering, computer science, or related field

Skills -

- Strong hands-on expertise in:

- Large language models (prompting, fine-tuning, evaluation, guardrails)

- Multimodal systems (vision-language models, speech interfaces)

- Knowledge grounding approaches (RAG, structured clinical knowledge systems)

- Direct experience with Software as a Medical Device (SaMD) development and lifecycle

- Familiarity with regulatory frameworks (e.g., FDA, CE, CDSCO, or equivalent)

- Thinks from first principles, able to reduce complex clinical and technical problems into clear, solvable components

- Takes ownership instinctively, following problems through from ambiguity to resolution without needing close direction

- Exercises sound judgment under uncertainty, especially where decisions carry clinical or patient safety implications

- Balances intellectual rigor with pragmatism, choosing approaches that work in real-world conditions rather than ideal settings

- Maintains a high internal bar for quality and safety, even when operating under pressure or incomplete information

- Adapts quickly to new domains, integrating clinical, technical, and contextual knowledge without rigid silos

- Communicates complex ideas clearly across disciplines, aligning engineers, clinicians, and stakeholders without oversimplifying risk

- Demonstrates intellectual honesty, acknowledging uncertainty, limitations, and failure modes in systems they build

- Operates with curiosity and skepticism, questioning assumptions and stress-testing solutions before deployment

- Builds trust within teams, setting clear expectations while supporting others to do high-quality, accountable work

- Remains grounded in end-user reality, consistently considering how systems will be used by frontline health workers in constrained environments

Prior Experience:

- At least 6 years of experience overall, including at least 1 year of significant work with LLMs and/or multimodal systems

- Prior experience leading and managing technical teams

- Demonstrated experience deploying AI systems in healthcare

- Demonstrated experience building or deploying AI/ML or digital health systems in low-resource or high-variability environments, with an understanding of constraints such as limited infrastructure, data quality challenges, and end-user usability in frontline care settings

- Experience in healthcare, digital health, or global health settingse, scalable, and impactful AI for healthcare; wed love to connect.