Mlops Jobs in Riverside, CA (NOW HIRING)

Overview

Utilize your expertise in machine learning, transformer architectures, foundation models, and telemetry-driven AI to impact millions of ArcGIS users worldwide. We are seeking a Sr. Data Scientist to join our Applied ML research team and help develop next-generation predictive AI capabilities for Esri's flagship products.

This role focuses on advancing state-of-the-art machine learning solutions through the development of custom transformer-based models and fine-tuned foundation models using telemetry data. You will lead experimentation with LoRA and QLoRA adapters, graph-based analytics, and AI-driven workflow automation to deliver transformational predictive capabilities.

This is an exciting opportunity to work at the intersection of machine learning research, product development, user experience, and large-scale telemetry analytics. You will collaborate closely with software engineers, product managers, UX designers, platform teams, and MLOps engineers to bring innovative AI capabilities into production and shape the future of GIS technology.

Responsibilities

• 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 DevOps 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

Requirements

• 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

Recommended Qualifications

• 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, LlamaSharp
• 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 large-scale AI inference systems with performance and cost optimization considerations

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