AI Research Scientist

• Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs).
• Reduce model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices.
• Apply and advance compression techniques such as quantization, knowledge distillation, and pruning.
• Build robust compression pipelines, establish performance and fidelity metrics, and address bottlenecks in production inference.

AI Research Engineer, Model Compression, Quantization

• Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
• Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
• Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
• Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
• Build and scale training workflows across distributed GPU infrastructure.
• Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
• Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
• Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
• Publish research findings in top-tier AI conferences and journals where applicable.

AI Research Engineer – Multi-Modal, Vision

• Conduct research on reinforcement learning algorithms for multimodal models,
• Design and build reinforcement learning infrastructure that supports scalable training,
• Develop and refine reward modeling strategies,
• Create and curate multimodal simulation environments and datasets,
• Analyze and optimize policy performance across modalities,
• Investigate and develop next-generation reinforcement learning paradigms,
• Publish research findings in top-tier conferences.

AI Research Engineer – Multi-Modal Reinforcement Learning

Machine Learning Engineer

• Be at the forefront of designing, developing, and implementing software solutions
• Responsible for implementing identified components based on design documentation
• Ensure implemented components are documented, unit-tested, and integration-ready
• Architect solutions, test code, and build CI/CD pipelines
• Provide work estimates for development tasks and guide relevant features and technologies
• Manage the end-to-end development of AI services and ensure best practices in MLOps/DevOps

Machine Learning Ops Engineer

• Drive innovation in model serving and inference architectures
• Optimize model deployment and inference strategies
• Work on resource-efficient models for limited hardware
• Engineer robust inference pipelines
• Establish comprehensive performance metrics
• Identify and resolve bottlenecks in production environments

AI Research Engineer – Kernel & Inference Optimization

AI Engineer

• Collaborare con altri GenAI Lead per condividere idee
• Sviluppare soluzioni tecniche per le proposte progettuali
• Guidare la creazione di asset all'interno del marketplace interno
• Creare materiali di apprendimento e fornire mentorship ai colleghi

GenAI Lead

• Own and drive the strategy and execution of our AI-powered Health Data Products portfolio.
• Define and own the global product strategy and roadmap for AI-powered Health Data Products.
• Lead, develop, and grow a team of product managers, fostering a culture of ownership, collaboration, and continuous improvement.
• Engage with executive stakeholders across clinical, commercial, and technical functions to align on priorities and ensure product-market fit.
• Champion a data-driven approach to product decision-making, OKRs, and KPIs.
• Engage directly with healthcare customers and internal stakeholders to understand workflows, pain points, and opportunities for AI-driven improvement.
• Ensure each product within the Health Data product portfolio is supported by a business case.
• Support in the identification and evaluation of third-party solutions, AI vendors, and integration partners relevant to the HIS space.
• Ensure collaboration with the quality assurance team to ensure products comply with relevant regulatory, data privacy, and clinical safety requirements.
• Communicate progress, risks, and strategic updates to relevant stakeholders.
• Oversee cross-portfolio dependencies and integration points across their portfolio.

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