Utilize your expertise in machine learning, transformer architectures, foundation models, and telemetry-driven AI to impact millions of ArcGIS users worldwide.Key ResponsibilitiesDrive the design, development, experimentation, validation, and deployment of AI models built from proprietary telemetry datasets, including both custom transformer architectures and foundation model adaptationsDevelop and fine-tune LoRA and QLoRA adapters for language models and sequence prediction systemsWork closely with DevOps and telemetry platform teams responsible for data ingestion, processing, and training infrastructureDesign and implement evaluation frameworks that measure model quality, calibration, throughput, latency, memory efficiency, and operational costConduct rigorous comparisons between custom-built models and foundation-model-based adapter solutions, providing recommendations on architecture and deployment strategyBuild and maintain automated testing and regression frameworks for both base models and adapter-specific functionalityDefine and document adapter contracts, including model configuration requirements, tokenizer expectations, input schemas, output behavior, and deployment assumptionsCollaborate with platform engineering, product management, UX, and MLOps teams to deliver production-ready AI solutions with clearly defined capabilities and performance targetsDefine training, validation, and benchmarking datasets to support model development and evaluationStay current on state-of-the-art developments in transformer architectures, parameter-efficient fine-tuning techniques, model serving technologies, and telemetry-based predictive systemsAuthor technical design documents, experiment reports, and best-practice guidance for model development and deploymentMentor software engineers, data scientists,



and analysts on model training, fine-tuning methodologies, telemetry-driven machine learning, and AI research practicesCollaborate with researchers and developers across Esri throughout the AI research and development lifecycleSolve and articulate complex technical challenges involving machine learning, predictive modeling, and user experience optimizationRequirements5+ years of professional software development, machine learning engineering, or data science experienceStrong applied machine learning background and deep understanding of modern transformer architecturesHands-on experience developing and training custom transformer-based models from scratchDemonstrated experience fine-tuning small and mid-sized language models using parameter-effective methods such as LoRA and QLoRAExperience building next-item and next-N prediction systems using telemetry, event, behavioral, or sequence dataExperience with PyTorch, Transformer architectures, Foundation models LoRA and QLoRA fine-tuning techniques Model evaluation and benchmarking methodologiesStrong analytical problem-solving skills and experience conducting research-oriented developmentExcellent written and verbal communication skillsAbility to communicate complex technical concepts to both engineering and product leadership audiencesBachelor's degree in Computer Science, Data Science, Mathematics, Artificial Intelligence,



or a related fieldMaster's degree or higher in Computer Science, Data Science, Mathematics, Artificial Intelligence, or a related fieldExperience with Esri ArcGIS products and geospatial technologiesExperience with adapter composition techniques, including weighted adapter merging, adapter routing, and mixture-of-adapters architecturesExperience with Sequence modeling and predictive analyticsExperience with ONNX Runtime, LlamaSharpExperience deploying and serving machine learning models in large-scale production environmentsExperience optimizing models for constrained environments, including CPU-only, edge, or low-memory GPU deploymentsFamiliarity with recommendation systems, ranking systems, and behavioral sequence modelingExperience with retrieval-augmented generation (RAG) and hybrid AI architecturesExperience with graph databases, graph analytics platforms, and graph-based machine learning techniquesFamiliarity with large-scale AI inference systems with performance and cost optimization considerations#LI-AL1#LI-OnsiteEsri'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.A reasonable estimate of the base salary range is #J-18808-Ljbffr

📌 Sr. Data Scientist- Ai Model Development (Ottawa)
🏢 NextGenEnergyJobs
📍 Ottawa