Sr. Data Scientist- AI Model Development
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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Total Rewards
Esri’s competitive total rewards strategy includes industry-leading health and welfare benefits: medical, dental, vision, basic and supplemental life insurance for employees (and their families), 401(k) and profit-sharing programs, minimum accrual of 80 hours of vacation leave, twelve paid holidays throughout the calendar year, and opportunities for personal and professional growth. Base salary is one component of our total rewards strategy. Compensation decisions and the base range for this role take into account many factors including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
The Company
At Esri, diversity is more than just a word on a map. When employees of different experiences, perspectives, backgrounds, and cultures come together, we are more innovative and ultimately a better place to work. We believe in having a diverse workforce that is unified under our mission of creating positive global change. We understand that diversity, equity, and inclusion is not a destination but an ongoing process. We are committed to the continuation of learning, growing, and changing our workplace so every employee can contribute to their life’s best work. Our commitment to these principles extends to the global communities we serve by creating positive change with GIS technology. For more information on Esri’s Racial Equity and Social Justice initiatives, please visit our website here.
If you don’t meet all of the preferred qualifications for this position, we encourage you to still apply!
Esri is an equal opportunity employer (EOE) and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. If you need reasonable accommodation for any part of the employment process, please email [email protected] and let us know the nature of your request and your contact information. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to from this e-mail address.
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