Why This Job is Featured on The SaaS Jobs
Applied Scientist roles are becoming central to modern SaaS as product differentiation increasingly comes from embedded AI rather than standalone features. This position sits in that shift, focused on production-facing research for voice and chat workflows in business communications, where latency, reliability, and measurable impact matter as much as model quality. The emphasis on real-time, multimodal and agentic systems also reflects a broader SaaS trend toward automating frontline work, not just analyzing it after the fact.
For a SaaS career, the value is the end-to-end exposure to how models become product: working with large-scale conversational data, evaluating performance with an eye on downstream outcomes, and collaborating with engineering, product, and design to deploy and monitor systems in production. Experience building LLM-based features and retrieval-driven capabilities translates well across SaaS categories that depend on knowledge work, support, sales, and operational automation.
This role tends to suit scientists who prefer applied R&D with clear constraints and who enjoy turning research themes into shippable capabilities. It also fits professionals who like cross-functional iteration and can balance experimentation with the discipline of evaluation, monitoring, and ongoing refinement in a live SaaS environment.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As an Applied Scientist at Dialpad, you'll be an integral part of our AI team, conducting R&D to power the next generation of autonomous voice agents and delivering features for transcribed voice and chat message data in the business communications domain. We have several research themes, including developing multi-modal, real-time agentic systems that can listen, reason, and take action during live customer interactions. We are also developing real-time knowledge retrieval models to power live coaching features for customer support and sales agents. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking applications.
If you're passionate about language, AI, and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us.
This position reports to the Manager of the NLP team and has the opportunity to be based in our Kitchener, ON, office.
What you’ll do
- Develop, implement, and refine state-of-the-art Natural Language Processing and Machine Learning algorithms for Dialpad's products.
- Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of the resultant business impacts.
- Manage massive textual data sets.
- Build advanced LLM-based features, including reasoning, multilingual and multimodal processing, and agents.
- Collaborate with cross-functional teams, including engineering, product, and design, to effectively deploy and scale models and algorithms in production.
- Submit papers to top-tier academic conferences and journals and contribute to the broader scientific community by reviewing submissions.
Skills you’ll bring
- Master’s or PhD degree in Linguistics, Computational Linguistics, Computer Science, Machine Learning, or related fields.
- 2+ years of NLP industry experience for Master’s degree holders or 1+ years for PhD degree holders.
- Demonstrated experience with machine learning, Python, PyTorch, and other relevant tools and technologies.
- A broad understanding of current LLM model architectures and techniques for tuning and optimizing LLMs.
- Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems.
- Excellent communication and collaboration skills to effectively work in a multi-disciplinary team.
- Familiarity with version control tools like Git for collaborative projects.
