• Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches. • Model surface reactions, interfacial degradation mechanisms, and electrochemical processes. • Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization. • Lead high-fidelity data generation campaigns.

• Conduct advanced simulations using DFT and MD for solid-state materials, with a focus on predicting properties for solid-state electrolytes and interfacial degradation reactions. • Employ data-driven approaches to analyze large datasets derived from computational simulations and experiments to uncover new insights into materials behavior. • Conduct high-fidelity data generation campaigns and develop ML force fields for solid-state materials. • Guide and scope projects with clear deliverables alongside agile teams. • Collaborate closely with multi-disciplinary teams to independently prototype and scale cutting-edge, impactful materials design solutions. • Generate and evaluate hypotheses to assist design decisions and influence project direction by developing and deploying computational methods and workflows. • Effectively present and communicate research findings through scientific talks, blog posts, client-oriented presentations, and peer-reviewed publications.

Role Description Invent, implement and deploy state-of-the-art machine learning and/or specific domain industry algorithms and systems. Build prototypes and explore conceptually new solutions. Work collaboratively with science, engineering, and product teams to identify customer needs in order to create and implement solutions, promote innovation and drive model implementations. - Applies data science capabilities and research findings to create and implement solutions to scale. - Responsible for developing new intelligence around core products and services through applied research on behalf of customers. - Develops models, prototypes, and experiments that pave the way for innovative products and services. - Build cloud services that work out of the box for enterprises (e.g., decision support, anomaly detection, forecasting and recommendations), natural language processing (NLP), Natural Language Understanding (NLU), Time Series, Automatic Speech Recognition (ASR), Machine Learning (ML), and Computer Vision (CV). - Design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. - Conversant on ethical problems in consideration of sciences. - Identify data science use cases and design scalable solutions that can be built as a feature of the product/service. - Contributes to writing production model code. - Work with Software Engineering teams to deploy them in production. - Set up environment needed to run experiments for all projects. - Set up distributed environments. - Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals. - Design and execute offline/online experiments and model performance testing. - Work with large, complex data sets. - Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. - Address business/customer problems and questions using statistical and machine learning techniques to achieve business goals and KPI's. - Come up with innovative solutions to address tradeoffs or challenges faced by team. - Stay up-to-date with research and trends regarding latest algorithms in ML or other industry/domain space. - Perform research in emerging areas, which may include efficient neural network development including quantization, pruning, compression and neural architecture search, as well as novel differentiable compute primitives. - May perform other duties as assigned. - May telecommute. Qualifications - Master’s degree in Computer Science, Engineering, or related technical field. - 4 years of experience in the job offered or in a computer-related occupation. Requirements - Deep Learning, Generative AI, LLM. - Python, C and C++. - Dataset design and curation at scale, and Synthetic data generation. - OpenCV, Git, and HuggingFace. - PyTorch, TensorFlow, and Keras. - Safety and Responsible AI evaluation and red teaming. - LLM evaluation and benchmarking, LLM-as-a-Judge calibration and validation. - Reinforcement Learning - DPO, PPO, KTO, and GRPO. - VLM, Diffusion models, image generation, VLM as a judge evaluation. - Datamining and statistical concepts. - ML libraries, scikit-learn and Pandas. - Statistical models for Machine Learning solutions, regression, classification and clustering. - Distributed systems. - Production operations and troubleshooting. - Technical proposals, design specs, architecture diagrams and presentations. Benefits - Flexible medical, life insurance, and retirement options. - Opportunities for community involvement through volunteer programs. Company Description Only Oracle brings together the data, infrastructure, applications, and expertise to power everything from industry innovations to life-saving care. And with AI embedded across our products and services, we help customers turn that promise into a better future for all. Discover your potential at a company leading the way in AI and cloud solutions that impact billions of lives. True innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing a workforce that promotes opportunities for all. We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling 1-888-404-2494 in the United States. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

• 12-month project to develop and compare approaches for optimizing cash denomination provisioning per withdrawal at ATM terminals • Formalization of the problem (variables, physical constraints, objective function) and a simulation environment using withdrawal history and Monte Carlo resampling • Development and comparison of three approaches — periodic offline policy; predict-then-optimize combined with reinforcement learning; short-horizon online MIP with a fallback • Coordination at the battery level (a set of terminals) to align depletion and reduce aggregated restocking • Evaluation against baselines (largest-denomination, proportional, threshold) using metrics for restocking events, time between restocks, service rate, latency, and traceability