• Conducts primary and secondary research of advanced and emerging healthcare informatics technology with a focus on Generative AI and emerging Artificial General Intelligence technology for healthcare treatment, payment, and operations use cases. • Creates research papers, analytical reports, proposals, and educational material. • Maintains a deep understanding of technology trends and hands-on experience with relevant technologies. • Develops generative AI models, architectures, databases, and systems to improve healthcare solutions. • Assists in developing and improving LLMs and ML tools used across the organization. • Communicates technology-related updates and requirements to other departments and contributes to presentations for senior management. • Collaborates with research and development teams, product management, and strategic analysts to support ongoing projects. • Partners with Business Units to provide reliable intelligence, validated technology options, and insights on enterprise and industry trends.

Role Description Your Responsibilities: - Conduct processes like repairs, inventories, and maintenance operations through Asset Management strategies, delivering savings to customers and additional business to Rockwell Automation. - In charge of implementing the techniques of Asset Management Professional to produce customer benefits and AMR business. - Create multiple Engineering projects and transactional services on behalf of Rockwell Automation and targeted Customers. - Provide monthly financial and savings reports. - Work with client departments including plant management, engineering, purchasing and distributors to identify efficiency plans for maintenance. - You will report to Asset Management Team lead. Qualifications - Degree in Engineering (Industrial, Mechanical, Electrical, or related field). Requirements - 2-3 years of experience in sales or business development within the industrial sector. - Negotiation skills. - Knowledge of sales techniques and market strategies. - Good level of English (Spoke, read, and write). - Schedule Availability. Benefits - Saving Fund. - Food coupons. - Major medical insurance. - Retirement benefit.

Role Description The R&D Engineer is a researcher-practitioner - someone equally comfortable reading papers and shipping prototypes. They are responsible for staying close to the frontier of LLM and AI systems research, identifying ideas with practical value, and building POCs that demonstrate feasibility. These POCs are demoed to the team and, where validated, handed off to the Platform and AI Engineers to productionize. This person does not work in isolation — they collaborate closely with the rest of the team throughout the research-to-product pipeline. What You'll Do: - Monitor and synthesize developments in LLM systems, AI infrastructure, and adjacent research areas - Prototype and evaluate novel ideas with a fast feedback loop — build, demo, iterate - Extend and improve the existing agent and RAG SDKs with new retrieval strategies, agent patterns, and provider integrations - Evaluate new embedding models, reranking approaches, and retrieval architectures against the existing pipeline - Collaborate with the Platform and AI Engineers to hand off validated POCs - Produce clear documentation and demos that make research accessible to non-researchers - Contribute to discussions with the tech lead on long-term research direction and where to place technical bets - Engage with the broader research community — conferences, papers, open-source projects - Identify opportunities where emerging techniques can address real problems surfaced by the AI Engineer Qualifications - Hands-on experience building or evaluating LLM systems — fine-tuning, inference, retrieval, or agents - Familiarity with RAG architectures beyond naive vector search — HyDE, MMR, graph-based retrieval, reranking, or hybrid search - Exposure to agent frameworks and multi-agent orchestration patterns (PydanticAI, LangGraph, AutoGen, CrewAI, or similar) - Demonstrated ability to move quickly from idea to working prototype - A portfolio of explored ideas — published work, technical blog posts, side projects, or OSS contributions - Strong communication skills — can explain complex research to non-researchers clearly - Collaborative by default; understands that research value is realized when ideas ship Requirements - Core: Python, agent frameworks (PydanticAI or equivalent), RAG pipeline design (embedding, retrieval, reranking), managed ML platforms (Vertex AI or equivalent) - Nice to have: Systems-level programming experience, familiarity with inference infrastructure (vLLM, TensorRT), experience with RLHF or fine-tuning pipelines, MCP protocol, graph databases (Neo4j) Company Description ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.

• Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality. • Explore and prototype improvements across spatial multimodal modeling, modality alignment, flow-based or diffusion-based video generation, and neural rendering-inspired representations to improve controllability and long-horizon consistency. • Improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression). • Define model training objectives that improve sim-to-real and real-to-sim generalization, especially for human motion, contact, and interaction dynamics across real-world and synthetic/simulation data. • Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments. • Translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams.