Principal Scientist leading research and development for Kraft Peanut Butter in Canada. Collaborating with teams to drive product development and ensure quality standards.
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.
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
Staff Applied Scientist at Afresh managing AI/ML models for fresh inventory control. Solving complex replenishment challenges to minimize food waste and enhance grocery decision - making.
Senior Threat Researcher analyzing Windows based malware behaviors and developing protection rules. Collaborating with teams to enhance Sophos' cybersecurity capabilities.
Principal Application Scientist solving complex business needs in scientific environments using Dotmatics SaaS products. Collaborating with clients and internal teams to deliver effective solutions in remote settings.
Senior Statistical Assistant in the UNESCO Institute for Statistics. Support SDG 4 coordination, data management, and administrative tasks for research initiatives.
Senior Machine Learning Scientist at Deep Genomics, developing innovative Biological Foundation Models. Collaborating with interdisciplinary teams to transform drug discovery using AI.
Lead R&D as a Staff Applied Scientist at Afresh, leveraging AI for grocery inventory control solutions. Your work will greatly impact food waste reduction and enhance food quality.
Senior Applied Scientist developing optimizing and ML models for inventory management at MaintainX. Shaping decision processes and interacting closely with product and design teams.
AI and Data Scientist at Verily enhancing healthcare solutions with AI and data - driven insights. Collaborating with experts to build scalable tools from complex clinical datasets.
Product Research Scientist at SandboxAQ translating scientific research into production - ready software solutions. Collaborating with ML and computational chemistry teams for drug discovery efforts.