AI Research Intern focusing on advancing deep learning techniques in financial products at TD. Collaborating on large - scale datasets and representing the team at ML conferences.
About the role
- Applied AI Scientist designing AI-driven applications to transform geospatial data into actionable insights. Collaborating with teams to develop end-to-end AI/ML pipelines in a cutting-edge environment.
Responsibilities
- Design, develop, and deploy AI-driven applications that transform large-scale geospatial data into actionable insights and predictive intelligence
- Build and operate end-to-end AI/ML pipelines including data ingestion, preprocessing, feature engineering, training, evaluation, and production inference
- Productionize reasoning models, vision-language models (VLMs), and multimodal AI systems that combine imagery, geospatial signals, and structured data
- Architect enterprise-grade training and experimentation frameworks, including automated pipelines, experiment tracking, benchmarking, and reproducible evaluation
- Create synthetic datasets and test harnesses to validate model performance, robustness, and edge-case behavior in real-world operational environments
- Work closely with domain experts, software engineers, product managers, and research partners to translate complex Earth intelligence challenges into deployable AI solutions
- Optimize models and inference systems for scalability, latency, cost efficiency, and reliability on modern cloud infrastructure
- Implement and maintain production inference systems, including monitoring, model versioning, retraining workflows, and performance tracking
- Stay current with the latest advances in foundation models, generative AI, multimodal learning, and reasoning systems, and translate research breakthroughs into practical systems
- Maintain high engineering standards through code reviews, documentation, experimentation discipline, and collaborative problem solving
- Help shape the next generation of Earth AI capabilities through collaboration with leading research organizations and technology partners.
Requirements
- MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, or a related technical field, or equivalent practical experience
- 5+ years of experience building and deploying machine learning systems in production environments
- Demonstrated experience designing and delivering end-to-end ML pipelines, including data processing, training automation, evaluation frameworks, and scalable inference
- Hands-on experience developing and deploying deep learning models, particularly in one or more of the following areas: Vision-language models (VLMs), Multimodal learning, Reasoning models, Large language models (LLMs), Computer vision or geospatial AI
- Strong programming skills in Python, with experience using modern ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience building reproducible experimentation pipelines, including model evaluation, dataset versioning, and experiment tracking
- Experience deploying models into production environments using modern cloud infrastructure and containerized systems
- Familiarity with distributed training, large-scale data processing, and model optimization techniques
- Ability to collaborate across research, engineering, and product teams to bring advanced AI capabilities into real-world applications.
Benefits
- robust 401(k) with company match
- mental health resources
- student loan repayment assistance
- adoption reimbursement
- pet insurance
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