Sr. Data Scientist- AI Model Development

Utilize your expertise in machine learning, transformer architectures, foundation models, and telemetry-driven AI to impact millions of ArcGIS users worldwide.

• Drive the design, development, experimentation, validation, and deployment of AI models built from proprietary telemetry datasets, including both custom transformer architectures and foundation model adaptations
• Develop and fine-tune LoRA and QLoRA adapters for language models and sequence prediction systems
• Work closely with Dev Ops and telemetry platform teams responsible for data ingestion, processing, and training infrastructure
• Design and implement evaluation frameworks that measure model quality, calibration, throughput, latency, memory efficiency, and operational cost
• Conduct rigorous comparisons between custom-built models and foundation-model-based adapter solutions, providing recommendations on architecture and deployment strategy
• Build and maintain automated testing and regression frameworks for both base models and adapter-specific functionality
• Define and document adapter contracts, including model configuration requirements, tokenizer expectations, input schemas, output behavior, and deployment assumptions
• Collaborate with platform engineering, product management, UX, and MLOps teams to deliver production-ready AI solutions with clearly defined capabilities and performance targets
• Define training, validation, and benchmarking datasets to support model development and evaluation
• Stay current on state-of-the-art developments in transformer architectures, parameter-efficient fine-tuning techniques, model serving technologies, and telemetry-based predictive systems
• Author technical design documents, experiment reports, and best-practice guidance for model development and deployment
• Mentor software engineers, data scientists, and analysts on model training, fine-tuning methodologies, telemetry-driven machine learning, and AI research practices
• Collaborate with researchers and developers across Esri throughout the AI research and development lifecycle
• Solve and articulate complex technical challenges involving machine learning, predictive modeling, and user experience optimization

• 5+ years of professional software development, machine learning engineering, or data science experience
• Strong applied machine learning background and deep understanding of modern transformer architectures
• Hands-on experience developing and training custom transformer-based models from scratch
• Demonstrated experience fine-tuning small and mid-sized language models using parameter-efficient methods such as LoRA and QLoRA
• Experience building next-item and next-N prediction systems using telemetry, event, behavioral, or sequence data
• Experience with PyTorch, Transformer architectures, Foundation models LoRA and QLoRA fine-tuning techniques Model evaluation and benchmarking methodologies
• Strong analytical problem-solving skills and experience conducting research-oriented development
• Excellent written and verbal communication skills
• Ability to communicate complex technical concepts to both engineering and product leadership audiences
• Bachelor’s degree in Computer Science, Data Science, Mathematics, Artificial Intelligence, or a related field
• Master’s degree or higher in Computer Science, Data Science, Mathematics, Artificial Intelligence, or a related field
• Experience with Esri ArcGIS products and geospatial technologies
• Experience with adapter composition techniques, including weighted adapter merging, adapter routing, and mixture-of-adapters architectures
• Experience with Sequence modeling and predictive analytics
• Experience with ONNX Runtime, Llama Sharp
• Experience deploying and serving machine learning models in large-scale production environments
• Experience optimizing models for constrained environments, including CPU-only, edge, or low-memory GPU deployments
• Familiarity with recommendation systems, ranking systems, and behavioral sequence modeling
• Experience with retrieval-augmented generation (RAG) and hybrid AI architectures
• Experience with graph databases, graph analytics platforms, and graph-based machine learning techniques
• Familiarity with…