About LyfPlus

Patients move between providers. Their care should continue—not start over.

LyfPlus is building connected healthcare infrastructure that helps pharmacies, hospitals, clinics and other providers coordinate referrals, prescriptions, follow-ups and patient journeys. Our work combines healthcare operations, provider software and network-level coordination to make care easier to navigate and more continuous.

We are building for the realities of existing healthcare systems

Our team works closely across product, engineering, clinical, commercial and expansion functions. We value people who combine strong professional judgment with curiosity, ownership and respect for the healthcare professionals and patients our technology serves.

About the role

LyfPlus is hiring two Applied AI & Machine Learning Engineers to design, build and evaluate production AI systems for healthcare coordination and operational decision support.

This is not a purely academic research position and not a conventional reporting-only data-science role. You will turn real healthcare and product problems into dependable AI-assisted workflows, while establishing the data, evaluation, monitoring and safety practices required to use these systems responsibly.

We welcome candidates whose strengths lean toward applied AI engineering, machine learning engineering or data science, provided they can connect modelling work to useful, production-ready healthcare products.

What you’ll do

• Identify healthcare and operational problems where AI or machine learning can create measurable value.
• Prototype, evaluate and productionize AI-assisted product capabilities.
• Develop systems for information extraction, classification, ranking, recommendation, forecasting or workflow assistance where appropriate.
• Build and improve data pipelines, feature pipelines, training workflows and evaluation datasets.
• Work with structured and unstructured healthcare, operational and product data.
• Design rigorous offline and online evaluation frameworks, benchmarks and quality metrics.
• Implement monitoring for accuracy, drift, latency, cost, reliability and harmful failure modes.
• Collaborate with product, engineering and clinical stakeholders to define appropriate human oversight.
• Document assumptions, limitations, model behavior and intended use.
• Help establish responsible-AI, privacy and security practices for sensitive healthcare applications.
• Integrate models and AI services into production applications and APIs.
• Communicate technical results clearly to both technical and non-technical colleagues.

What we’re looking for

• Strong practical experience in applied machine learning, AI engineering, data science or a closely related discipline.
• Proficiency in Python and common data or machine-learning libraries.
• Experience taking at least one model, AI workflow or data product beyond experimentation into a production or operational environment.
• Strong understanding of data preparation, experimentation, evaluation and statistical reasoning.
• Experience working with APIs, databases, version control and software-engineering practices.
• Ability to distinguish a compelling demonstration from a reliable production system.
• Strong written communication, documentation and cross-functional collaboration skills.
• Ability to work on-site with the Nairobi team.

Valuable, but not required

• Experience with natural-language processing, large language models, retrieval systems, ranking systems or time-series forecasting.
• Experience with MLOps, model serving, experiment tracking, feature stores or data orchestration.
• Familiarity with healthcare data, clinical workflows or regulated products.
• Experience evaluating AI systems for bias, hallucination, uncertainty, safety or human-in-the-loop performance.
• Familiarity with privacy-enhancing methods, de-identification or secure handling of sensitive data.
• Graduate-level training in computer science, machine learning, statistics, mathematics, engineering or a related field.

Success in this role

Within your first months, you should be able to identify a well-scoped production use case, establish its baseline and evaluation framework, deliver a working implementation and clearly demonstrate whether it improves the relevant healthcare or operational outcome.