Senior Applied AI Software Engineer (US hours)

About M3:  A Japanese global leader in the provision of ground-breaking and innovative technological and research solutions to the healthcare industry. The M3 Group operates in the US, Asia, and Europe with over 5.8 million physician members globally via its physician websites which include mdlinx.com, m3.com, research.m3.com, Doctors.net.uk, medigate.net, and medlive.cn. M3 Inc. is a publicly traded company on the Tokyo Stock Exchange (jp:2413, NIKKEI 225) with subsidiaries in major markets including the US, UK, Japan, South Korea, and China, and in 2020 was ranked in Forbes’ Global 2000 list. The M3 Group provides services to healthcare and the life science industry. In addition to market research, these services include medical education, ethical drug promotion, clinical development, job recruitment, and clinic appointment services. M3 has offices in Japan, UK, France, Germany, Brazil, Sweden, China, USA, and South Korea, as well as India.

Due to continued growth, M3 MR is seeking a Senior Applied AI Software Engineer 

Business Unit Mission:

M3 MR, part of M3 Inc., provides the most comprehensive and highest-quality healthcare market research recruitment, data collection, and insight support services globally. With proprietary access to healthcare professionals and patient communities across more than 70 countries, M3 MR partners with pharmaceutical, biotech, medical device, and market research agencies to deliver trusted, compliant, and decision-ready insights at speed and scale.

M3 MR holds ISO 20252, ISO 27001, and ISO 27701 certifications, reflecting the group’s commitment to data quality, respondent integrity, information security, and operational excellence across quantitative and qualitative methodologies. The group combines deep healthcare expertise, advanced technology platforms, and global operational reach to support clients across the full research lifecycle.

Role Purpose

Building production systems powered by software engineering, data science and modern AI.

We are seeking a hands-on Senior Applied AI Software Engineer to design, build and deploy production systems that combine software engineering, data science, large language models (LLMs), agentic AI and intelligent automation. This is a software engineering role first and an AI role second. We are looking for someone who enjoys building real systems, shipping code and solving business problems, while applying modern AI technologies where they create meaningful value. You will work alongside Data Scientists, Engineers, IT teams and Business Analysts to transform ideas, models and business requirements into robust, scalable solutions. This includes operationalising statistical and decision-science models, building AI-powered products and workflows, selecting and integrating foundation models, optimising cost and performance, and developing agent-based systems that automate and enhance complex business processes. Initial focus areas include digital twin platforms, operational decision-support systems, AI-enabled research products, intelligent workflow automation, Copilot-style experiences and agentic AI solutions embedded across the organisation. Success in the role will be measured by the delivery of reliable, maintainable production systems that create measurable business value, rather than experimentation alone.

Key Responsibilities

Software Engineering & Product Delivery

Design, build, test and maintain production software systems.

Develop scalable services, APIs, integrations and automation workflows.

Contribute to architecture, engineering standards and best practices.

Support deployment, monitoring and continuous improvement of production solutions.

Applied AI & Intelligent Systems

Design and deploy AI-enabled solutions using LLMs, agentic architectures and automation.

Build production AI applications, copilots and workflow solutions.

Develop prompt and context engineering frameworks.

Implement RAG, tool-calling, memory and orchestration patterns.

Evaluate and select appropriate AI models and technologies.

Optimise latency, quality, reliability and cost.

Monitor, test and continuously improve AI system performance.

Data & Decision Science Enablement

Operationalise statistical, predictive and AI models.