Hybrid Applied Scientist

at ShyftLabs

• Job title: Applied Scientist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: CA$110,000 - CA$150,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Google Cloud Platform, Python, PyTorch
• Location requirements: Hybrid, Toronto, Canada

About the role

• Applied Scientist leveraging applied research to design production AI systems for Fortune 500 companies. Focusing on knowledge-centric AI, graph-based learning, and advanced RAG architectures.

Responsibilities

• 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.

Requirements

• 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.

Benefits

• 100% coverage for health, dental, and vision insurance for you and your dependents from day one.