Staff Applied Scientist at Loopio

Toronto, Ontario, Canada

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

TechnologyIndustries

Requirements

6+ years of experience applying ML in production, with specialization in NLP, LLMs, RAG/RAC, agent workflows, and knowledge graphs
Demonstrated success in fine-tuning and deploying large-scale models, optimizing them for retrieval, reasoning, and personalized generation
Strong grounding in experimental design, evaluation methods, and advanced modeling techniques
Ability to set vision and strategy for applied ML, ensuring alignment with business and product objectives
Experience mentoring senior engineers and data scientists, fostering technical rigor and scientific excellence
Skilled in influencing cross-functional priorities and representing the ML/AI domain at the company level
Expertise in architecting large-scale ML systems that combine retrieval, knowledge graphs, and agent orchestration
Strong fundamentals in Python, ML frameworks (PyTorch, TensorFlow), and distributed computing (Spark, Databricks, Ray)
Proven ability to build APIs, microservices, and integrations that bring ML models into production with high reliability
Track record of balancing cutting-edge ML with pragmatic engineering practice

Responsibilities

Lead research-to-production efforts in NLP, LLMs, retrieval-augmented generation (RAG), multi-agent reasoning, and knowledge graph–driven solutions
Architect and deploy advanced ML models, including LLM fine-tuning, reasoning agents, and context-aware generation pipelines
Explore and adapt state-of-the-art research into production-ready prototypes, ensuring solutions are both innovative and practical
Build intelligent workflows that combine agentic planning, tool orchestration, and continuous feedback loops to improve automation, personalization, and accuracy
Own the scope, architecture, and delivery of scalable ML systems that support high-performance content retrieval and personalized answer generation
Design and integrate knowledge graphs, embeddings, and hybrid retrieval methods to improve coverage, precision, and context awareness
Build reliable services by combining strong software engineering fundamentals (Python, APIs, distributed systems) with modern ML frameworks
Ensure ML systems are production-ready, balancing cutting-edge research with scalability, maintainability, and performance
Drive end-to-end project plans for modeling initiatives, establish milestones, and ensure high-quality, timely delivery
Collaborate with Product, Engineering, and Design teams to integrate ML capabilities into user-facing workflows, validate outcomes, and deliver measurable customer value
Mentor senior engineers and data scientists, building depth in applied ML, NLP, and AI system design
Represent the ML/AI function in company-wide forums, shaping best practices, technical direction, and long-term roadmap priorities

Skills

Key technologies and capabilities for this role

PythonNLPLLMsRAGKnowledge GraphsEmbeddingsHybrid RetrievalAPIsDistributed SystemsLLM Fine-TuningMulti-Agent Orchestration

Questions & Answers

Common questions about this position

What experience level is required for this Staff Applied Scientist role?

The role requires 6+ years of experience applying ML in production, with specialization in NLP, LLMs, RAG/RAC, agent workflows, and knowledge graphs.

Is this position remote or hybrid?

The position is hybrid.

What technical skills are needed for this role?

Key skills include expertise in NLP, LLMs, retrieval-augmented generation (RAG), multi-agent reasoning, knowledge graphs, Python, APIs, and distributed systems.

What does the role involve in terms of leadership and collaboration?

You will mentor senior engineers and data scientists, collaborate with Product, Engineering, and Design teams, and represent the ML/AI function in company-wide forums to shape best practices and technical direction.

What makes a strong candidate for this position?

A strong candidate has demonstrated success in fine-tuning and deploying large-scale models, leading research-to-production efforts in advanced ML areas, and owning scalable ML systems with strong software engineering skills.

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

Benefits

Remote Work Options

Hybrid Work Options

Phone/Internet Stipend

Professional Development Budget

Risks

Increased competition from established players like SAP threatens Loopio's market share.

Potential over-reliance on CRM integrations poses risks if policies change.

Recent layoffs may indicate internal financial or strategic challenges.

Differentiation

Loopio integrates seamlessly with CRM systems like Salesforce and HubSpot.

The Response Management Loop Framework offers a holistic approach to proposal management.

Loopio's smart content management system centralizes and organizes knowledge efficiently.

Upsides

Growing demand for AI-driven content management boosts Loopio's market potential.

Integration with CRM systems streamlines sales and proposal workflows.

Expansion into new markets enhances Loopio's global presence and capabilities.

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