Job Description

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PATH is a global nonprofit dedicated to achieving health equity. With more than 40 years of experience forging multisector partnerships and with expertise in science, economics, technology, advocacy, and dozens of other specialties, PATH develops and scales up innovative solutions to the world’s most pressing heath challenges.

PATH is seeking an AI Engineer to help scale SnapiForm, an AI-powered platform available through Telegram mini-app, WhatsApp and the browser that enables health workers to digitize paper HMIS forms by simply taking a photo. Following a successful pilot in the DRC that significantly improved data accuracy and reduced reporting time, SnapiForm is now expanding to process millions of health records each month. In this role, you will develop and optimize computer vision and Vision-Language Model (VLM) pipelines for handwriting recognition, table extraction, and structured data parsing, while building scalable and cost-efficient AI systems for low-resource health settings.

Responsibilities:

• Design and optimize AI pipelines for complex document understanding. Focus on extracting structured data from mobile-captured HMIS forms, specifically tackling challenges like handwriting recognition, complex table extraction, and multilingual parsing.

• Research, benchmark, and fine-tune state-of-the-art Vision-Language models (e.g., Qwen-VL) and foundational OCR models on domain-specific datasets. Utilize advanced techniques (LoRA/QLoRA, DeepSpeed) to maximize accuracy on noisy, real-world mobile images.

• Architect and deploy production-grade inference pipelines using vLLM or similar engines. Optimize continuous batching, KV cache management, and quantization to maximize throughput while strictly maintaining our low per-page processing cost targets.

• Design architecture for both self-hosted/local cloud environments (like Linode) and on-premise hardware, keeping data sovereignty and cost efficiency in mind.

• Tune AI models for visual data optimization. Develop strategies for image chunking, tiling, and preprocessing to allow models to efficiently process high-resolution images and large, complex tables without losing context.

• Evaluate, select, and provision optimal cloud and on-prem GPU infrastructure to handle a target volume of 10 million forms.

• Assess next-generation hardware (e.g., NVIDIA Blackwell nodes) to balance massive scalability, performance, and budget efficiency.

• Lay the technical groundwork for future iterations, including offline/edge processing support, expanded multilingual capabilities, and interoperability beyond DHIS2.

• Willingness to travel to PATH countries as needed and overlap with GMT and ESA timezones

Required Qualifications and Experience:

• Education B.S. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.

• Experience 7+ years of experience in Machine Learning Engineering, with at least 1-2 years specifically focused on Computer Vision, Document AI, or Multimodal Large Language Models.

• Core Frameworks Deep expertise in PyTorch and the Hugging Face ecosystem (Transformers, PEFT).

• Inference Engines Hands-on, production-level experience deploying models using vLLM.

• Domain Expertise Proven experience working with Document AI, Optical Character Recognition (OCR), Handwriting Recognition (HTR), or Vision-Language models.

• Image Processing Proficiency in computer vision libraries (OpenCV, Pillow) and experience handling real-world, variable-quality mobile images, including tiling and chunking strategies.

• Infrastructure & Cloud Strong experience with Docker, Kubernetes, and cloud GPU provisioning. Familiarity with distributed training and inference optimization.

• Programming Exceptional Python skills, with experience writing clean, modular, and highly optimized code.

• Language Fluency in verbal and written English

Personal Attributes:

• Passionate about building technology that improves health systems and supports frontline health workers in low-resource settings.

• Strong focus on building cost-effective, scalable AI solutions that perform well on limited hardware.

• Able to balance cutting-edge AI research with practical engineering decisions and real-world constraints.

• Proactive and able to work independently as well as collaboratively.

• Strong sense of accountability and commitment to continuous improvement.

What We Offer:

• Opportunity to contribute to impactful digital health and data initiatives.

• Competitive compensation and flexible working arrangements.