Applied AI Engineer

myTomorrows

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 business owner, be hardened by Product & Engineering, or be stopped. * Stay up to date with the rapidly evolving AI engineering landscape and translate relevant developments into practical opportunities for myTomorrows. WHAT YOU BRING TO THE TABLE * 3+ years of professional software engineering experience, ideally in a product, platform, backend, fullstack, startup, or scale-up environment. * Strong software engineering fundamentals. You have built and maintained real systems before (and before vibe-coding was thing), and you know that shipping reliable software is about more than generating code. * Experience with backend development, ideally with Python and modern API development. * Experience with relational databases such as MySQL, PostgreSQL, or Oracle. * Experience with cloud platforms such as AWS, Azure, or GCP. * Strong understanding of testing, code review, observability, security, documentation, and maintainability. * Hands-on experience with the modern essentials of AI agents, agentic engineering and AI coding tools: LLM APIs, agents, RAG, MCPs, Skills, workflow automation and AI-enabled product development. * Ability to work independently in ambiguous environments, while communicating clearly with technical and non-technical stakeholders. * Strong product sense: you care about solving the actual problem, not just using the newest tool. * A pragmatic startup mentality: you can move fast, make sensible trade-offs, and know when to prototype, when to harden, and when to stop. * Excellent English communication skills. NICE TO HAVE * Experience with FastAPI, Pydantic, SQLAlchemy, uv, ruff, pre-commit, GitHub Actions, or similar tools. * Experience with AWS-native architectures, infrastructure as code, Terraform, serverless, Kubernetes, or cloud-native platform work. * Experience with frontend development, especially Angular, React, TypeScript, or fullstack personal projects. * Experience building internal tools, developer tools, workflow automation, or operations tooling. * Experience with AI evaluation frameworks, model monitoring, prompt/version management, or human-in-the-loop review systems. * Experience with healthcare, pharma, clinical research, regulated environments, privacy-sensitive data, or compliance-heavy workflows. * Experience with tools such as Claude Code, Codex, Cursor, GitHub Copilot, LangChain/LangGraph, LlamaIndex, n8n, or similar AI/workflow platforms. WHAT SUCCESS IN THE FIRST 6 MONTHS LOOKS LIKE * You have built trust with Product, Engineering, DevOps, Data, and business stakeholders by delivering useful AI-enabled solutions without creating unmanaged technical debt. * You have delivered 2-3 high-impact AI Acceleration missions, such as an internal tool, workflow automation, agentic engineering improvement, or AI-assisted product delivery pilot. * You have shipped production-capable software with clear ownership, tests, documentation, observability, and security considerations. * You have created reusable templates, prompts, scripts, or examples that other teams can use. * You have helped teams understand where AI is genuinely useful, where it is not ready yet, and what guardrails are needed. * You have contributed to a culture where AI-assisted engineering is judged by objective production outcomes: correctness, security, maintainability, observability, and business value. CURRENT TECH STACK We are fully cloud-native, leveraging AWS and adopting a lean, API-first product development approach driven by modern cloud technologies and thoughtful design practices. Our current Backend Engineer posting describes a stack including Python, FastAPI, Pydantic, SQLAlchemy, MySQL/PostgreSQL, Angular, GitHub Actions, Renovate, ruff, uv, pre-commit, Docker, Docker Compose, Kubernetes, Terraform, DynamoDB, and Neo4j. As Applied AI Engineer focused on internal use cases, you will often work in lighter-weight stacks like scripts, Skills, MCPs, APIs, workflow tools, internal dashboards rather than full product infrastructure. Familiarity with the product stack is useful context, but your day-to-day tooling will be shaped by the mission. WHAT WE OFFER * Impactful work that helps patients gain access to potentially lifesaving treatments. * A unique opportunity to help define how a HealthTech scale-up becomes AI-native. * International work environment, scale-up energy, and a flat organizational structure that encourages creativity and accountability. * Competitive salary, annual performance bonus, and an Employee Stock Option Plan. * Great career development opportunities in a fast-growing company. * Learning and development budget alongside internal knowledge-sharing sessions. * Attractive pension plan, full premium covered by myTomorrows. * Hybrid working model. * Policies to support working parents. * Healthy lunch at the thriving Amsterdam office. * Unlimited access to professional guidance by certified psychologists via OpenUp. * Monthly events hosted by our vibrant Culture Club as well as an annual myTomorrowland company-wide celebration. Equal opportunities myTomorrows is an Equal Opportunity Employer and, beyond upholding discrimination-free practices, we are committed to cultivating a workplace where difference and diversity are protected and celebrated. The best work comes from our best selves, and we go to great lengths in supporting our team members to be just that.

Seamflow

ABOUT THE JOB We’re removing one of the last great barriers to progress in the physical world. While software moves fast, industries like aerospace, healthcare, and energy are still slowed by outdated, manual systems. We are on track to raise our Series A, backed by a 14× oversubscribed $4.5m seed round from Tier-1 VCs such as Coinbase, Reddit, Klarna, and Spotify. Our world-class team includes former unicorn founding engineers and talent from X, Google, Amazon, and Yandex. We value people who want to win and thrive in high-ownership environments. We’re building the system that lets complex, real-world work move at software speed. WHO WE ARE LOOKING FOR We're looking for an Applied AI Engineer who thinks in user workflows and model behavior, not benchmarks or isolated prompts. This is a role for someone who wants to decide what AI should do, build it end-to-end, and put it in users' hands. You will fit right in if you have experience with: * Applied AI — Design and build LLM-powered features that solve real workflows, from prompt to production * Model Behavior — Reason about why models fail, when to use which model, and how to make them reliable in production * Evals & Iteration — Build evals that actually catch regressions and drive improvements, not vanity metrics * Agents & Tooling — Design multi-step agents, tool use, and orchestration for complex domain workflows * Document Intelligence — Extract structured data from messy real-world documents (PDFs, scans, forms, tables) * Product Judgment — Be opinionated about what AI should do, what it shouldn't, and when "good enough" ships WHAT YOU WILL DO * Ship from Day 1 — Deploy AI features to production immediately and own a real user-facing surface area * Own it end-to-end — From problem definition to prompts, evals, monitoring, and iteration * Solve real problems — Sit in customer calls, look at real documents, debug bad outputs, and make the system better WHAT WE ARE LOOKING FOR TECHNICALLY * Strong experience with Python and modern LLM tooling (OpenAI/Anthropic/Azure OpenAI SDKs, structured outputs, function calling) * Experience building production AI systems beyond a wrapper around a chat API * Comfortable with evals, prompt engineering, and reasoning about model failure modes * Ability to work across the stack to integrate AI into real product workflows (TypeScript/React frontend, Python backend) * Ability to reason about complex domain workflows, not just prompt-response Strong plus if you have: * Founder or ex-founder experience * Early-stage or stealth startup background * Founder fellowships or similar environments (EF, YC, Antler, etc.) * Experience building AI-powered products in production * Experience working where requirements were unclear and evolving YOUR FIRST MONTH * Ship an AI feature to production from day one * Own problems end-to-end, from prompt to eval to deployment * Do what needs doing, including looking at hundreds of real outputs, debugging edge cases, and customer interaction WE OFFER * Pace and purpose — In-person in London (visa sponsorship available) * No friction — Relocation, visas, meals, and tools fully covered * A tight-knit team — High standards, high trust, no politics

MTR Legal

WIR sind MTR Legal Rechtsanwälte. Mittelständisch und international. Wir beraten im gesamten Familienrecht. Ein starker Wegbegleiter für nationale und internationale Mandanten. Wir verfolgen nicht nur Leistung, sondern schaffen auch Raum für Persönlichkeiten, um gemeinsam an einem Strang zu ziehen. Denn wir wissen: Mit Ihnen bringen wir unser Beratungsniveau für unsere Mandanten auf das nächste Level! DU Was du dafür können musst * LLM/RAG in Produktion (nicht nur Demo) * Dokumentenfokus (OCR/Extraktion/Klassifikation) oder sehr nah dran * Engineering: Versionierung, Repro, Tests, Monitoring * Evaluation-Mindset: Qualität messen & Regression verhindern Was am Ende stehen muss (Deliverables) * DocTypes + Extraktionsschemas (Top 10) inkl. Validierungen * OCR/Extraktionspipeline (Edge Cases: schlechte Scans, Tabellen, Stempel) * RAG/LLM: Summary + Mandanten-E-Mail-Draft + Action Items * Confidence/Checks: Thresholds, Rule/LLM-Checks, Human-in-the-loop Gates * QA/Eval: Golden Set, Metriken (Precision/Recall), Prompt/Model Regression Tests CONNECTING THE DOTS Tech (Orientierung) OCR/Extraction, LLM/RAG, Embeddings, Prompting/Templates, Eval Harness (Golden Sets), Monitoring/Drift. Integration in Workflow/Queue. Rahmenbedingungen * 100% Remote (CET/Europa ideal) * Projektbasis, Start asap, Auslastung nach Vereinbarung PERSPECTIVE Produktionsreife Document Intelligence: DocType → Felder → Summary → Draft → Checks. Wichtig: messbare Qualität, Regression-Schutz, sichere Gates (Human-in-the-loop).