Staff Applied Scientist

Toronto, Ontario, Canada

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Productivity Software, Artificial Intelligence, AI & Machine LearningIndustries

Requirements

Candidates should have 6+ years of experience applying Machine Learning in production, with a specialization in NLP, LLMs, RAG, agent workflows, and knowledge graphs. Proven success in fine-tuning and deploying large-scale models, optimizing them for retrieval, reasoning, and personalized generation, along with a strong grounding in experimental design, evaluation methods, and advanced modeling techniques is required. Expertise in architecting large-scale ML systems combining retrieval, knowledge graphs, and agent orchestration, strong fundamentals in Python, ML frameworks (PyTorch, TensorFlow), and distributed computing (Spark, Databricks, Ray), and a proven ability to build APIs, microservices, and integrations are also necessary. The ability to set vision and strategy for applied ML, mentor senior engineers and data scientists, and influence cross-functional priorities is essential.

Responsibilities

The Staff Applied Scientist will lead research-to-production efforts in NLP, LLMs, RAG, multi-agent reasoning, and knowledge graph-driven solutions, and architect/deploy advanced ML models including LLM fine-tuning, reasoning agents, and context-aware generation pipelines. They will explore and adapt state-of-the-art research into production-ready prototypes, build intelligent workflows combining agentic planning, tool orchestration, and continuous feedback loops, and own the scope, architecture, and delivery of scalable ML systems for content retrieval and personalized answer generation. Responsibilities also include designing and integrating knowledge graphs, embeddings, and hybrid retrieval methods, building reliable services with strong software engineering fundamentals and modern ML frameworks, ensuring ML systems are production-ready, driving end-to-end project plans for modeling initiatives, collaborating with Product, Engineering, and Design teams, and mentoring senior engineers and data scientists.

Skills

NLP

LLMs

Retrieval-Augmented Generation (RAG)

Multi-agent reasoning

Knowledge Graphs

ML model fine-tuning

Reasoning agents

Context-aware generation

Agentic planning

Tool orchestration

Python

APIs

Distributed systems

Software engineering

Loopio

RFP response software for enterprises

About Loopio

Loopio specializes in simplifying the process of responding to Requests for Proposals (RFPs), Requests for Information (RFIs), Due Diligence Questionnaires (DDQs), and Security Questionnaires. Its main product is RFP response software that helps businesses manage and automate the intricate task of creating high-quality responses. The software features a smart content management system that organizes a company's knowledge base, making it easy for teams to collaborate, assign tasks, and review projects efficiently. Loopio operates on a subscription-based model, allowing clients to access its software and tools for a recurring fee. This model helps clients save time and improve the quality of their responses, enabling them to win more business. Loopio stands out from competitors by focusing on enhancing collaboration and efficiency for medium to large enterprises across various industries, including technology, healthcare, and finance.

Toronto, CanadaHeadquarters

2014Year Founded

$203MTotal Funding

GROWTH_EQUITY_VCCompany Stage

Data & Analytics, Consulting, Enterprise SoftwareIndustries

201-500Employees