Job Summary

• 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.