Staff Applied Scientist, Loopio
3 days a week in office
RFP software provider
Open for applications
RFP software provider
201-500 employees
Open for applications
3 days a week in office
201-500 employees
Company mission
To empower clients to shine in every response.
Role
Who you are
- 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 practices for scale and sustainability
- Proven ability to partner with Product, Engineering, and Design leaders to translate technical opportunities into customer value
- Skilled at framing ML solutions in terms of user impact, ROI, and strategic advantage
- Experience shaping AI capabilities that improve answer accuracy, personalization, and workflow efficiency for end users
What the job involves
- We are looking for an accomplished Staff Applied Scientist to join the Library team within ProductOrg, leading the design and delivery of ML-driven capabilities that power the heart of Loopio’s platform
- This role is central to advancing how we retrieve content, generate high-quality and personalized answers, and enable intelligent agent workflows that transform customer efficiency and outcomes
- You will drive innovation across semantic retrieval, retrieval-augmented generation, multi-agent orchestration, and knowledge graph–driven approaches to deliver answers that are accurate, context-aware, and measurable in impact
- As a Staff-level leader, you will not only solve complex technical problems but also mentor senior talent, influence product direction, and set best practices for applied ML across the organization
- 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
Our take
Loopio provides RFP response software to companies in a variety of industries including healthcare, financial services and telecommunications. Its platform streamlines responses to proposal requests, requests for information and due diligence questionnaires, helping firms to improve sales efficiency and unlock growth opportunities.
The company boasts an impressive client list including DocuSign, PayPal and American Express. Loopio expects its client base to continue growing, bolstered by a 2023 independent study that shows that clients of the company can experience a three-year return on investment alongside almost $6 million in business benefits.
Loopio has used funding to accelerate product development and customer acquisition, as well as expand its team. It has also partnered with Door, a digital due diligence platform for asset management and research teams. This partnership exemplifies a mature caution in Loopio's expansion aims, helping them to keep in control of their growing business.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
9% employee growth in 12 months
Company
Funding (2 rounds)
Apr 2021
$200m
GROWTH EQUITY VC
Feb 2018
$8.7m
SERIES A
Total funding: $208.7m
Company benefits
- Robust Health Benefits
- Extended Parental Leave
- Employee Stock Options
- Professional Mastery Budget
- Remote Work Subsidies
- Annual Company Retreat
- Generous Paid Time Off Program
- Team Socials
Company HQ
Downtown Yonge East, Toronto, ON
Leadership
Zak Hemraj
(Co-Founder & CEO)Prior to co-founding Loopio, Zak spent 8 years at Achievers, joining as a Software Developer before moving into the role of Sales Engineer an then Manager and Team Lead of Software Development.
Matt York
(Co-Founder & CTO)Matt previously worked as a VP of Engineering at Top Hat and as a Software Developer & Manager at Achievers.
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