Why This Job is Featured on The SaaS Jobs
Applied Scientist roles are becoming a core differentiator in SaaS, particularly as vendors embed AI directly into customer-facing workflows rather than treating it as a side project. This position sits in that product-led AI layer, working on real-time, multimodal, and agentic systems in a business communications context where latency, reliability, and interpretability matter. The emphasis on production deployment alongside research themes signals a SaaS environment where model quality is judged by user outcomes, not only offline benchmarks.
For a long-term SaaS career, the standout value is end-to-end exposure to how machine learning becomes a maintained product capability. Experience here translates across modern SaaS companies grappling with LLM evaluation, monitoring, cost-performance tradeoffs, and data governance at scale. The combination of experimentation, distributed training, and cross-functional delivery builds a portfolio that maps to applied AI leadership paths in product, platform ML, or AI-first feature development.
This role suits scientists who prefer applied work with clear product constraints and who can collaborate tightly with engineering, product, and design. It aligns well with candidates who enjoy iterating from prototypes to production systems, and who want their research instincts to be shaped by real customer interactions and operational feedback loops.
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, and we are advancing DialpadGPT, our proprietary domain-adapted LLM, to drive orchestrated agentic workflows and handle complex task execution. 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 evaluations and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts.
- Conduct experiments in the distributed training and optimization of LLMs.
- 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.
- Contribute to the development of DialpadGPT, ensuring high accuracy, cost-effectiveness, and optimal performance in serving at scale.
- 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.
