Applied Scientist

Toronto, OntarioFull-TimeMid-levelOther

Skills

Python Neo4j AWS GCP Machine learning Deep learning PyTorch LLM A/B testing Problem solving Documentation MLflow RAG Large language models Embeddings Instruction tuning LoRA Retrieval-augmented generation Agent orchestration

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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