IBMData Scientist-Artificial Intelligence

1. Generative AI Model Development: -Design and develop generative AI models, including language models, image generation models, and multimodal models. -Explore and implement advanced techniques in areas such as transformer architectures, attention mechanisms, and self-supervised learning. -Conduct research and stay up-to-date with the latest advancements in the field of generative AI. 2. Data Acquisition and Preprocessing: -Identify and acquire relevant data sources for training generative AI models. -Develop robust data preprocessing pipelines, ensuring data quality, cleanliness, and compliance with ethical and regulatory standards. -Implement techniques for data augmentation, denoising, and domain adaptation to enhance model performance. 3. Model Training and Optimization: -Design and implement efficient training pipelines for large-scale generative AI models. -Leverage distributed computing resources, such as GPUs and cloud platforms, for efficient model training. -Optimize model architectures, hyperparameters, and training strategies to achieve superior performance and generalization. 4. Model Evaluation and Deployment: -Develop comprehensive evaluation metrics and frameworks to assess the performance, safety, and bias of generative AI models. -Collaborate with cross-functional teams to ensure the successful deployment and integration of generative AI models into client solutions. 5. Collaboration and Knowledge Sharing: -Collaborate with data engineers, software engineers, and subject matter experts to develop innovative solutions leveraging generative AI. -Contribute to the firm's thought leadership by presenting at conferences, and participating in industry events.

· Lead strategic analysis on global trends in Physical AI, including autonomous driving, embodied intelligence, robotics, and sensorimotor foundation models. · Continuously monitor and benchmark key players (e.g., Tesla FSD, Waymo Robotaxi) and emerging technologies in North America and globally. · Conduct exploratory research on cutting-edge technologies related to world models, multimodal large models, and planning frameworks in AI. · Produce high-quality technical and strategic insight reports for internal stakeholders to inform roadmap and business decisions. · Collaborate with R&D and product teams to shape future research directions and support decision-making with clear, data-driven foresight. · Participate in academic engagements, including paper drafting and submission to top-tier AI conferences (e.g., NeurIPS, CVPR, ICRA, RSS).

We empower our people to stay resilient and relevant in a constantly changing world. We're looking for people who are always searching for creative ways to grow and learn. People who want to make a real impact, now and in the future. Does that sound like you? Then it seems like you'd make a great addition to our vibrant international team. DAI AIX – AI Acceleration and Exploration, is working on the cutting-edge research of Data Analytics and AI with Siemens global technology network, and consulting, co-creation, data driven applications for the end customers. Research Scientist is to do applied research for Industrial AI applications in the team. We are seeking a Reinforcement Learning (RL) Specialist to lead the design, implementation, and optimization of RL-driven systems for post-training of foundation models. The primary focus of this role is advancing our RL capabilities for real-world applications such as industrial control systems and LLM agents. You will develop cutting-edge algorithms, improve post-training efficiency, and deploy scalable RL solutions in industry. You'll make an impact by • Research on state-of-the-art data analytics & AI technologies on a general range. • Mainly focus on modern foundation model applications in industrial scenarios1. Context engineering for foundation models2. Development of agent systems for industrial applications3. Task-specific model finetuning • Partially work with multi-modal applications • Participating in both internal & external research projects • Assist deployment of customer development/deployment project

Bringing the State of the Art to Products  Collaborates with and bridges the gap between researchers (in community, Microsoft Research [MSR], or in their own organizations) and development teams. Brings new technology and approaches into production by applying long-term research efforts to solve immediate product needs.  With limited guidance from others, works to create product impact. Identifies approach, and applies, improves, or creates a research-backed solution (e.g., novel, data driven, scalable, extendable) to positively impact a Microsoft product or service. Solves components or aspects of a problem as assigned by a senior team member. May publish research to promote receiving new intellectual property for product impact.  Participates in collaborative relationships with relevant product and business groups inside or outside of Microsoft and provides expertise or technology to create business impact. Participates in technology transfer attempts, filing patents, authoring white papers, developing or maintaining tools/services for internal Microsoft use, or consulting for product or business groups. May publish research to promote receiving new intellectual property for business impact.  Capability Management and Networking  Maintains ties with external network of peers and identifies prospective talent, when asked. May contribute to publications on research findings. May participate in candidate interviews. Collaborates with the academic community to develop the recruiting pipeline and establish awareness of their work.  Reinforces a positive environment by applying best practices. May support mentorship by assisting with onboarding of research interns or other entry-level team members, if applicable.  Documentation  Performs documentation of work in progress, experimentation results, plans, etc. Documents scientific work to ensure process is captured. Participates in the creation of informal documentation and may share findings to promote innovation within group.  Ethics and Privacy  Understands and follows ethics and privacy policies when executing research processes and/or collecting data/information.  Leveraging Applied Research  Applies strategy by understanding the role in the team and applying the strategy provided by senior team members and incorporates state-of-the-art research. Asks probing questions to better understand strategy.  Researches and develops an understanding of tools, technologies, and methods being used in the community that can be utilized to improve product quality, performance, or efficiency. Contributes knowledge around several specialized tools/methods to support the application of business impact or serves as an expert in a deeply specialized area.  Gains deep knowledge in a service, platform, or domain and acquires knowledge of changes in industry trends and advances in applied technologies. Consults with engineers and product teams to apply advanced concepts to product needs. Learns product domain by reviewing products.  Machine Learning Functionality, Insights, and Technical Tools  Prepares data to be used for analysis by reviewing criteria that reflect quality and technical constraints. Reviews data and suggests data to be included and excluded. Describes actions taken to address data quality problems. Assists with the development of useable datasets for modeling purposes. Supports the scaling of feature ideation and data preparation. Helps take cleaned data and adapts for machine learning purposes, under the direction of a senior team member. Seeks guidance from senior team members when confronted with problems/challenges.  Uses machine learning algorithms that structures, analyzes, and uses data in product and platforms to train algorithms for scalable artificial intelligence solutions before deploying. Begins to develop new machine learning improvements independently while under the direction of a senior team member.  Collaborates to leverage data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem. Uses statistical analysis tools for evaluating Machine Learning models and validating assumptions about the data while also reviewing consistency against other sources. Begins to independently run basic descriptive, diagnostic, predictive, and prescriptive statistics. Assists with the communication of insights under the direction of senior team members.  Supports the application and use of intelligence created during the training of algorithms for deployment. Seeks information about large-scale computing frameworks, data analysis systems, and modeling environments to improve models. Helps create a model, apply the model to real products, and then verify effects through iterations. Helps with experiments by putting multiple models in production and evaluating their performance. Sets up monitoring and implementation to track production models, under the direction of a senior team member. Addresses models when that break, under the direction of others.  Leverages or designs and uses machine learning/data extraction, transformation, and loading (ETL) of pipelines (e.g., data collection, cleaning) based on data prepared.