Job Description

Rhino Federated Computing Rhino solves one of the biggest challenges in AI: seamlessly connecting siloed data through federated computing. The Rhino Federated Computing Platform (Rhino FCP) serves as the ‘data collaboration tech stack’, extending from providing computing resources to data preparation & discoverability, to model development & monitoring - all in a secure, privacy preserving environment. To do this, Rhino FCP offers flexible architecture (multi-cloud and on-prem hardware), end-to-end data management workflows (multimodal data, schema definition, harmonization, and visualization), privacy enhancing technologies (e.g., differential privacy), and allows for the secure deployment of custom code & 3rd party applications via persistent data pipelines. Rhino is trusted by >60 leading organizations worldwide - including 14 of 20 of Newsweek’s ‘Best Smart Hospitals’ and top 20 global biopharma companies - and is leveraging this foundation for financial services, ecommerce, and beyond. About the Role We are seeking a scientist with deep expertise in drug discovery and computational chemistry to drive the real-world application of Rhino’s Federated Computing Platform across pharma and biotech R&D. This is a hands‑on, client‑facing role for a scientist leader who understands the balance between scientific innovation, data privacy, and intellectual property protection. You will partner directly with computational and medicinal chemists, demonstrating how federated learning can accelerate discovery without compromising confidentiality. You’ll lead implementations, deliver measurable outcomes, and transform curiosity about federated AI into everyday adoption across research teams. Key Responsibilities Scientific Partnership & Technical Implementation Lead hands‑on technical engagements with pharma users, guiding them from initial evaluation to deep integration of the Federated Computing on their active drug discovery programs. Serve as the primary scientific partner for computational and medicinal chemists, helping them benchmark federated models and build trust in the results. Design and deliver technical workshops and training sessions to empower new technical users (fine tuning, federated training) and non‑technical users (inference applications), showcase best practices, and accelerate adoption across research teams. Serve as a forward‑deployed scientific expert, combining technical depth with commercial awareness to drive platform adoption and demonstrate measurable impact. Identify and cultivate internal champions within partner organizations to scale adoption across scientific teams and therapeutic programs. Create scientific case studies, proof‑of‑value reports, and conference materials that showcase platform success and strengthen customer partnerships. Cross‑Functional Collaboration & Product Insight Act as the voice of the user, capturing and translating field insights into actionable recommendations for Rhino’s product and engineering teams. Collaborate with product managers to shape the scientific roadmap, ensuring alignment with evolving computational chemistry and AI challenges. Develop and maintain a library of user‑facing materials — example datasets, evaluation protocols, and best‑practice guides — to standardize scientific excellence across deployments. Required Skills Deep Domain Expertise: A PhD (or equivalent experience) with hands‑on experience in structure‑based drug discovery within a pharmaceutical or biotech environment. You must have practical experience with techniques such as molecular docking, molecular dynamics, virtual screening, protein design related data analysis. AI/ML Application Experience: Practical experience applying machine learning models to solve problems in drug discovery. You don't need to build models from scratch, but you should understand how they are validated and used. Exceptional Communication Skills: The ability to explain complex scientific and technical concepts (including AI/ML and federated learning) clearly and concisely to diverse audiences, from bench scientists to R&D leadership. Technical Proficiency: Strong scripting skills (e.g., Python) and familiarity with common computational chemistry software suites (e.g., Schrödinger, MOE, OpenEye, RDKit). Preferred Qualifications Federated Learning Knowledge: A foundational understanding of the principles behind federated learning, data privacy, and the challenges of working with decentralized data. Enterprise Software Experience: Familiarity with the process of deploying and supporting software in secure, on‑premise, or virtual private cloud (VPC) enterprise environments. User‑Facing Experience: Proven experience in a client‑facing scientific role such as Application Scientist, Field Scientist, Scientific Liaison, or a similar position where you were responsible for user training, technical support, or collaborative research projects. Location Boston or San Francisco. Consider other locations for highly qualified candidates only. We will sponsor visas for USA PhD candidates in the relevant areas - Mathematics, Artificial Intelligence, Federated Learning, Computational Chemistry/Biology/Biomedical candidates. An B.S./M.S. with extensive, directly relevant industry experience will also be considered. #J-18808-Ljbffr Rhino Federated Computing

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