Applied AI Engineer

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About myTomorrows myTomorrows is a global health tech company dedicated to breaking down barriers for patients seeking treatment options. We strive to enable earlier and better treatment access by bridging the gap between those searching for possible options, and the companies who develop them. We work closely with patients, healthcare professionals, trial sites, patient advocacy groups, and BioPharma - connecting key stakeholders in the drug development ecosystem. We've developed a cutting-edge AI-powered technology platform that simplifies and streamlines access to drugs in development. To support our users and clients, we have a range of industry-expert specialized teams ready to help. Our services include clinical trial patient recruitment, Expanded Access Program management and Real-World Data collection. With a global footprint spanning 134 countries, to date we've supported over 17,000 patients, 3,000 physicians and 350 sites, earning the trust of 60+ BioPharma companies. In October 2025, we closed a €25M investment with Avego Healthcare Capital to fuel our global ambitions and scale the business. Join us in shaping the future of treatment access - making tomorrow's therapies accessible for people who need them today. The opportunity: Applied AI Engineer As Applied AI Engineer at myTomorrows, you will help us become more AI-native in how we work, build, and deliver value to patients, physicians, trial sites and (bio-)pharma partners. Your primary focus is internal: the teams, workflows, and operational processes inside myTomorrows. This is not a pure research role, and it is not a "prompt engineer" role. You are an experienced software engineer who knows how to build reliable systems, and who is experienced with the possibilities created by LLMs, agents, AI-assisted coding, retrieval, workflow automation, and internal tooling. You will work as part of our AI Acceleration initiative: a small, high-leverage team focused on safely applying AI across the organization. You embed with internal teams, e.g. Operations, Regulatory, Commercial, Marketing, to find the highest-leverage points of intervention, build production-ready solutions, and leave behind systems that teams can own and operate without you. A key part of the role is not just building things yourself, but helping myTomorrows learn how to build with AI. You will help define reusable patterns, guardrails, evaluation approaches, and engineering practices so that AI-assisted work becomes reliable, secure, maintainable, and scalable. Given that most of our team is located in the Netherlands, we only consider candidates for this position who live within commuting distance of our office in Amsterdam. How you work Each engagement follows the model of a "Forward Deployed Engineer": Insertion. When you start a mission, you sit with the people who do the actual work: not the people who manage them. You watch, you ask questions, and you map what's really happening: the tools, the manual steps, the tribal knowledge, the workarounds that nobody questions anymore. Your first deliverable is not code. It is a situational awareness map. Discovery. From that map, you identify the highest-leverage intervention point: not the most technically interesting problem, but the one that, if solved, would make the most visible difference to the most people in the shortest time. Delivery. You build production-capable solutions on real data with measurable success criteria defined upfront. You identify an internal champion in week one who will own the system after you leave, and you embed them in the work from the start. Handoff. You leave behind a running system, not a prototype. You leave behind an eval framework and runbook so the system doesn't rot. And you leave behind an AI substrate - connectors, pipelines, and workflow patterns - that makes the next mission faster. What you'll do in this role Build production-capable AI-enabled internal tools, workflow automations, agents, and integrations that solve real business problems by embedding with internal teams. Use modern LLM capabilities such as structured outputs, tool calling, retrieval-augmented generation, agentic workflows, prompt/context engineering and creating evals. Help teams translate ambiguous business problems into clear, testable, AI-assisted delivery plans. Build backend services, APIs, integrations, and internal applications using modern software engineering practices. Work closely with Product, Engineering, QA, DevOps, Data, Legal, Privacy, and Security to ensure AI-built software is safe, secure, observable, and maintainable. Design and implement evaluation approaches for AI systems, including test sets, human review loops, quality criteria, failure mode analysis, and monitoring. Create reusable playbooks, templates, prompts, Skills, MCPs and examples that help other teams adopt AI effectively. Help assess whether a solution should remain a lightweight internal tool, be transferred to a busin

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