Team Lead, Federated AI - Forward Deployed

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.

The company is headquartered in Boston, with an R&D center in Tel Aviv.

Forward Deployed AI team – Rhino Federated Computing Platform

As the Lead for the Forward Deployed AI Team, you will lead a hands-on, customer-facing AI engineering and scientist group responsible for deploying and scaling Rhino’s Federated Computing Platform (FCP) in complex, real-world environments. You will work directly with customers’ technical and research teams — often embedding within their workflows — to design, implement, and optimize federated AI models and systems that balance innovation with privacy, regulatory compliance, and performance. You will guide a team of applied AI engineers through federated model training, inference, deployment, and monitoring, while helping customers unlock the full potential of federated data collaboration. Your leadership will shape how federated AI is operationalized at scale — in production, under privacy and security controls with measurable business impact. 

• Lead the design and delivery of federated AI solutions across imaging, text, and structured data domains.
• Design and implement federated training and inference pipelines, integrating data preprocessing, model orchestration, and AIOps tooling.
• Own project execution from scoping to deployment — ensuring clarity, speed, and technical excellence.

• Partner directly with customers to understand workflows, map data ecosystems, and design scalable AI solutions.
• Serve as a trusted technical advisor on privacy-preserving ML, model optimization, and deployment best practices.
  Build long-term relationships that drive adoption and measurable outcomes for customer programs.

• Manage multiple concurrent engagements, balancing strategic leadership with hands-on problem-solving.
  Provide mentorship to Forward Deployed Engineers and Scientists, fostering a high-performance, learning-driven culture.
  

• Identify reusable solution patterns and contribute to the standardization of federated AI workflows.
• Surface customer insights and field feedback that shape Rhino’s roadmap.
• Collaborate with product and research teams to prototype and validate new federated learning research and frameworks.

• Experience: 5+ years in applied AI, machine learning engineering, or MLOps roles; experience deploying models in production environments.
  
• Technical Expertise: Deep hands-on experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and AI/MLOps ecosystems.
  
• Generative AI Tools: Familiarity with LLM frameworks (GPT, Gemini, Claude, LangChain, agentic frameworks, etc.).
  
• Programming & Systems: Strong proficiency in Python, REST APIs, and data systems (SQL/NoSQL); comfort with cloud computing and GPU-based systems.
  
• Delivery Focus: Proven ability to own technical outcomes end-to-end, from scoping and prototyping to full-scale delivery.
  
• Collaboration: Excellent communication skills and the ability to operate across customer, product, and engineering teams.
  
• Mindset: Action-oriented, adaptable, and able to prioritize effectively in fast-changing environments.

• Industry experience in healthcare, biopharma, financial services, and public sector.
  
• Ph.D. or M.S. in a quantitative field (Computer Science, Engineering, Biomedical Informatics, Bioinformatics, Chemistry, or related).
  
• Prior experience deploying federated learning, privacy-preserving AI, or secure distributed computing systems.

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.