Applied Scientist

What You'll Be Doing

• Conduct applied research to solve real-world problems using LLMs, graph-based models, and multimodal AI.
• Rapidly understand problem context, constraints, and success metrics, and design pragmatic AI solutions aligned with product and business goals.
• Design hybrid AI architectures combining knowledge graphs, vector search, and deep learning for reasoning-aware systems.
• Research and implement graph embeddings, graph attention networks (GATs), and graph neural networks (GNNs) for representation learning and inference.
• Design and build advanced RAG systems at scale, going beyond naïve vector similarity search.
• Implement hybrid semantic retrieval across vector stores and graph databases (e.g., entity-aware retrieval, path-based reasoning, graph-augmented RAG).
• Optimize retrieval pipelines for latency, relevance, grounding, and explainability in production environments.
• Fine-tune LLMs and embedding models for domain-specific tasks (instruction tuning, adapters, LoRA, etc.).
• Design and implement LLM agent systems, including multi-agent orchestration strategies, tool use, planning, and memory.
• Evaluate, iterate, and optimize agent architectures to solve complex, multi-step enterprise workflows efficiently.
• Build and fine-tune document extraction pipelines, including (OCR systems, Layout-aware models, Vision-Language Models (VLMs), Multimodal document understanding and classification)
• Design scalable pipelines for enterprise document ingestion, enrichment, indexing, and retrieval.
• Build end-to-end AI pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Partner with platform and data engineering teams to productionize solutions on AWS or GCP.
• Monitor model performance, detect drift, and drive continuous improvement strategies.
• Design evaluation frameworks, offline metrics, and online experimentation (A/B testing) to measure real-world impact.

What You'll Bring

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Data Science, or a related field.
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred).Hands-on experience with applied research and translating research ideas into production-grade AI systems.
• Proven experience with knowledge graphs, graph embeddings, or graph neural networks.
• Experience building advanced RAG systems using vector databases and structured knowledge sources.
• Strong understanding of LLMs, embeddings, and fine-tuning techniques.
• Experience deploying AI systems in enterprise or large-scale production environments.
• A product-oriented, problem-solving mindset with the ability to quickly learn new domains and design efficient AI solutions under real-world constraints.
• Solid foundation in ML fundamentals, statistics, and experimentation.

Nice to Have

• Experience with graph databases (e.g., Neo4j, Neptune) and vector stores.
• Experience with agent-based LLM systems and multi-agent strategies.
• Experience with MLOps tools (SageMaker, Vertex AI, MLflow, feature stores).
• Familiarity with time-series forecasting, recommendation systems, or personalization models.
• Prior experience contributing to open research or collaborative academic projects

What Sets This Role Apart

• Publications in top-tier conferences or reputed journals
• Strong research background with peer-reviewed papers
• Experience publishing in leading AI/ML venues (e.g., NeurIPS, ICML, ICLR, CVPR)
• Demonstrated research impact through high-quality academic publications

Salary Range

• $110,000 - $150,000 (CAD)