Applied AI Engineer, Public Health LLMs at Resolve to Save Lives

Resolve to Save Lives was created in 2017 by Dr. Tom Frieden, former director of the US Centers for Disease Control and Prevention (CDC). Through his work in government and with philanthropy, Dr. Frieden pinpointed a unique problem: no one is thinking about how to systematically combat the world’s leading killers. Heart disease carries the distinction of being the world’s #1 cause of death. And few organizations raise their focus from one disease to look at epidemics as a whole—and how they can be prevented. Resolve to Save Lives is committed to saving lives from these preventable causes by partnering with government and civil society to implement scalable, proven strategies.

We prioritize partnerships with national and local organizations in low- and middle-income countries to co-create, advocate for, and scale up activities in heart disease prevention and epidemic preparedness. We work with global organizations to produce and promote policies that maximize health gains. Our funders include Bloomberg Philanthropies, the Bill & Melinda Gates Foundation and Gates Philanthropy Partners, which is funded with support from the Chan Zuckerberg Foundation. We are resolved to save lives.Position Purpose

We are looking for a passionate Applied AI Engineer to join our innovative team, focusing on the exciting field of Large Language Models (LLMs) in the context of Public Health. In this role, you will lead the design, fine-tuning, deployment, and evaluation of AI/ML systems based on pre-trained models (e.g., LLaMA, Mistral, GPT, Phi) that help ease the lives of healthcare workers and clinicians. You will work closely with back-end and mobile engineers to bring cutting-edge AI capabilities to life.
The ideal candidate will possess the expertise to leverage existing Large Language Models (LLMs) to train and evaluate models using program-specific clinical data (e.g., patient notes, SMS interactions, training materials or health worker feedback) and deploy within RTSL's digital health tools and global EHRs (e.g., Simple, BP Passport).Additionally, there is a strong likelihood of developing an open-source, locally runnable, adapted LLM to address cost and confidentiality concerns.

You'll Be Working At The Intersection Of Cutting-edge AI And Grassroots Public Health. This Is An Opportunity To Shape The Future Of Digital Health Tools That Are Open Source, Impactful, Real-world Solutions For Some Of The Most Underserved Populations Globally. Our Primary Use Cases For LLMs Are Anticipated To Include (not Limited To)

Generating patient summaries specifically tailored for healthcare workers.
A chatbot for appointment scheduling.
Develop a predictive model to enhance and automate existing workflows.
Optimized worklists for frontline workers.
On-the-job training and ready-reckoner tools for healthcare professionals.

Length of Engagement: This is a two-year fixed-term appointment with the possibility of extension based on available funding and mutual interest.

Core Responsibilities

The ideal candidate will perform duties and responsibilities such as, but not limited to, the following:

Research, evaluate, and implement state-of-the-art LLMs.
Fine-tune pre-trained models for specific tasks and datasets.
Develop and deploy AI applications using Python.
Perform data manipulation and analysis using Pandas to prepare data for model training and evaluation.
Design and evaluate prompt engineering strategies for optimizing LLM outputs in specific public health contexts.
Collaborate with cross-functional teams to integrate AI solutions into existing products and workflows.
Stay up to date with the latest advancements in AI, particularly in the LLM space.
Apply responsible AI principles, including fairness, privacy, and transparency, especially in clinical and community health settings.
Manage and lead the AI pilots/projects at RTSL.
Train and upskill other engineers on the team.

Qualifications
Education

Bachelor's or Master's degree in Computer Science, Engineering, Machine Learning or a related field

Experience

8 years of software development experience
3-5 years of experience in training and using AI models.
Proven track record of using Large Language Models (LLMs) and building Predictive Models to meet user requirements
Experience collaborating with multi-disciplinary and cross-functional teams
Delivered LLM-based solutions in resource-constrained environments
Hands-on experience with pre-trained AI models.
Experience working in healthcare or public health settings (strong plus)
Contributed to or maintained open-source AI/ML projects (strong plus).
Familiarity with MLOps including model serving, performance monitoring, and lifecycle management, particularly in low-bandwidth or edge environments i(s a plus).

Skills & Abilities

Strong proficiency in Python programming.
Strong experience in data manipulation using Pandas, NumPy, and data preprocessing techniques
Familiarity with pre-train models
Skilled in machine learning frameworks (e.g., TensorFlow, PyTorch).
Familiarity with AI Tools (Hugging Face, LangChain, ONNX, etc.)
Strong understanding of AI ethics, data privacy, and bias mitigation techniques
Excellent analytical and problem-solving skills
Ability to communicate complex technical ideas clearly to non-technical stakeholders
Ability to prototype and iterate quickly
Comfortable working in agile, interdisciplinary teams across geographies