• Design and build machine learning models to support healthcare monitoring and data analysis • Develop AI solutions that ensure patient privacy while tracking behavioral patterns • Implement smart alert systems to assist healthcare professionals in timely decision-making • Create and maintain anomaly detection models for health-related data • Work closely with software engineering teams to integrate AI functionalities into applications • Ensure all AI systems adhere to healthcare data security and compliance standards • Continuously optimize models to enhance performance and accuracy • Stay up to date with and apply the latest AI advancements

• Build and lead a team of platform engineers, setting technical direction, conducting code reviews, and mentoring engineers as the team grows • Define and build the application-layer foundation for AI at Roofr — establishing the core integration patterns, agent architecture, and shared abstractions that product teams across the org will build on • Own the integration architecture strategy — making the foundational decisions on how Roofr's product connects to AI services and third-party systems, and setting the standard for how the rest of engineering builds with AI • Design and maintain our third-party integration layer, establishing scalable, reliable patterns for connecting Roofr to external services and APIs • Build the data pipelines that power our AI features — structured data feeds, embeddings, retrieval infrastructure, and more • Take strong ownership over platform reliability, performance, and developer experience — the teams building on top of you depend on it • Partner with product and engineering leadership to shape the AI roadmap and help the broader organization understand what's possible • Navigate a maturing codebase and help drive architectural consistency across domains with varying levels of maturity • Establish standards and best practices for how Roofr builds with AI — from SDK design to observability and reliability • Evaluate emerging models, frameworks, and tooling and make pragmatic, well-reasoned bets on adoption

• You'll manage and scale paid campaigns across Meta, TikTok, and Google — and help expand into newer channels like Reddit, Snapchat, and Quora. • Day to day that means setting up and optimizing campaigns, translating performance data into creative briefs, keeping reporting accurate and up to date, and flagging what's working and what isn't. • If you study ads while scrolling, save hooks that catch your eye, and naturally think about why certain creatives convert — this role was built for you.

Role Description We are looking for a technical lead to own delivery of our large molecule AI model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, protein engineering, and federated learning. You will lead teams building and operationalizing large-scale ML systems for: - Antibody modeling - Co-folding - Developability prediction - Biologics discovery You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. Your responsibilities include: - Setting technical direction and driving execution - Challenging modeling decisions and turning ambiguity into executable plans - Managing risks and dependencies - Mentoring senior engineers and ML scientists - Diving into technical depth when needed We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems. Qualifications - PhD, MSc, or equivalent experience in a relevant field - 5+ years applying ML to complex scientific or biological problems - Experience in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design - Hands-on experience with modern ML systems in Python and PyTorch - Experience with large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar Requirements - MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows - Ability to define success criteria and validate model quality - Experience leading delivery of complex ML projects - Comfortable operating as a player-coach, mentoring while contributing directly to modeling, experimentation, or architecture - Ability to work effectively with product, research, leadership, customers, and scientific stakeholders Nice to have - Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments - Experience on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments - Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar Benefits - Industry-competitive compensation, including early-stage virtual share options - Remote-first working – work where you work best - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget - Generous holiday allowance - Office Days at our Berlin HQ or a different European location (3x per year) - A high-calibre, execution-focused team with experience from leading organizations