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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data Scientist - **Company:** Albertsons Companies - **Location:** Pleasanton, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Nvidia CUDA, Distributed Computing Environment, Fraud Prevention and Detection, Graph Database, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Data Streaming, Google Cloud, Graph Neural Networks, Pytorch, Large Language Models, Apache Spark, Deep Learning, Generative AI, Agentic-AI, Pandas, Data Lakes, Pyspark, Scikit Learn, Kubernetes, Information Technology, Low Latency, Apache Kafka, Free and Open-Source Software, Triton Inference Server, Machine Learning Operations, Hardware Infrastructure, Feature Extraction, Docker, Network Optimization, Databricks - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_fd75a46a73147e1c6914682fa9f9f63a ## About the Role * PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Operations Research, or a related quantitative field strongly preferred. * Master's degree with exceptional equivalent industry research and leadership experience may be considered., * 12 plus years of experience developing and deploying machine learning and deep learning solutions in production environments. * 8 plus years of experience leading advanced deep learning research, experimentation, and enterprise-scale implementation efforts. * Demonstrated experience operating as a Principal Scientist, Principal AI Engineer, Distinguished Engineer, Research Lead, or equivalent senior technical individual contributor., * Deep Learning * Graph Neural Networks (GNNs) * Graph Representation Learning * Graph Embeddings * Knowledge Graphs * Graph Transformers * Foundation Models and LLMs Expert-level hands-on proficiency in PyTorch and/or TensorFlow. Extensive experience building large-scale distributed machine learning solutions using: * Apache Spark * PySpark * Databricks Lakehouse * Delta Lake * MLflow * Unity Catalog * Expert knowledge of model training, optimization, serving, and lifecycle management across distributed GPU environments. * Proven experience designing enterprise-grade inference systems balancing accuracy, latency, reliability, scalability, and operational cost. * Advanced proficiency in Python and machine learning ecosystems including NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, and related frameworks Research & Thought Leadership * Strong publication record in leading machine learning, artificial intelligence, graph machine learning, or data science conferences and journals. * Proven track record translating advanced research into measurable business outcomes. * Demonstrated technical leadership through patents, publications, conference presentations, open-source contributions, or recognized industry thought leadership. Preferred Qualifications Deep expertise in advanced graph learning architectures including: Graph Convolutional Networks (GCNs) GraphSAGE Graph Attention Networks (GATs) Heterogeneous Graph Neural Networks Temporal Graph Networks Graph Transformers * Experience building and deploying LLM, generative AI, retrieval-augmented generation (RAG), multimodal AI, and agentic AI solutions. * Experience operating large-scale AI platforms on AWS, Azure, and/or Google Cloud Platform. * Experience with Ray, Kubernetes, Docker, Nvidia CUDA ecosystem, Triton Inference Server, and distributed GPU infrastructure. * Experience designing streaming inference architectures using Kafka, Structured Streaming, and real-time decisioning systems. * Experience leading enterprise AI transformations across multiple business units. * Background in retail, e-commerce, supply chain, customer intelligence, recommendations, fraud detection, network optimization, or large-scale graph applications. * Recognized industry subject matter expert with demonstrated influence beyond immediate organizational boundaries ## Description * Lead the design and development of end-to-end deep learning solutions, from problem formulation and model architecture selection through deployment and continuous optimization within the Databricks Lakehouse platform. * Serve as the technical authority for Graph Neural Networks (GNNs), graph representation learning, and advanced deep learning architectures, driving innovation and adoption across high-impact business problems. * Architect scalable machine learning and deep learning platforms, establishing reusable frameworks, standards, and best practices for model development, deployment, and lifecycle management. * Define and implement robust evaluation, explainability, and model governance frameworks to ensure model quality, transparency, and business impact. * Lead the development and optimization of distributed training, large-scale data processing, and inference systems using Spark and modern AI infrastructure. * Drive the fine-tuning, adaptation, and product ionization of foundation models, large language models (LLMs), and other advanced AI architectures leveraging enterprise data assets. * Partner with Data Science, Engineering, Product, and Business leaders to translate strategic opportunities into scalable AI solutions that deliver measurable value. * Influence AI strategy, technical roadmaps, and architectural decisions while mentoring senior practitioners and advancing organizational AI capabilities.