Your role
We are hiring AI Systems Engineers to help build that machinery. This role is for engineers who like consequential junctions: between training outputs and deployable artifacts, between runtime systems and safe release, between quality claims and evidence, and between ambitious AI plans and systems that can actually carry them.This is not a research role, and it is not a generic support role. It is an implementation-heavy, building-focused engineering role on a small team responsible for making in-house AI capabilities easier to package, evaluate, deploy, promote, operate, and improve.

AI Platform Engineering exists to shorten the path from emerging AI capability to reliable production impact. We build the shared systems, standards, and delivery pathways that let in-house models and AI capability packages move from candidate state into observable, rollback-safe production operation. Our work sits at the junction between model development, runtime systems, evaluation, and delivery. We enable the broader AI Platform division by making it faster and safer to ship new capabilities, improve existing ones, and learn from production behavior.

This is a new team. The systems, interfaces, and standards are still being shaped. The work is highly consequential, highly practical, and closely tied to the company’s broader AI strategy. We are not building one-off demos or isolated launches. We are building the machinery by which a growing AI organization can repeatedly deliver real capability into production.

This is a full-time, in-office position based out of our Kitchener location. Daily on-site attendance is required. 

What you’ll do

You will help design, build, and improve the systems that connect AI capability development to production reality.

Depending on your strengths, that may include work such as:

• Improving how model and capability artifacts are packaged, versioned, promoted, and rolled back.
• Building or improving deployment and release pathways for AI-backed services.
• Enabling shadow-serving, staged rollout, and candidate-versus-incumbent comparison.
• Strengthening runtime behavior, observability, and debugging for model-backed systems.
• Building or automating evaluation systems that make release decisions evidence-based.
• Reducing bespoke coordination and strengthening the shared rails used by multiple AI teams.

The exact balance will depend on your background and the team’s evolving needs. What will not vary is the mission: your work should make the broader AI Platform organization faster, safer, and more effective at turning in-house AI capability into production reality.

Skills you’ll bring

• Bachelor's degree in Computer Science, Engineering, or equivalent related experience.
• 2 to 6 years of professional software engineering experience, with a proven track record of shipping production infrastructure or real systems that matter.
• Experience in writing solid, maintainable production code and applying strong software engineering fundamentals to solve complex debugging challenges.
• Experience in operating within ambiguous, cross-functional environments where requirements evolve and interfaces are real.
• Expertise in building for reproducibility, operability, and rollout safety, focusing on the quality of change rather than just local implementation.

Nice to have

• Experience with cloud infrastructure, containerized environments, managed ML platforms, or service orchestration systems.
• Experience with model serving, deployment systems, experiment tracking, artifact/version management, or ML lifecycle tooling.
• Experience with distributed systems, service platforms, search/relevance systems, internal enablement tooling, or production AI platforms.
• Experience with testing, benchmarking, experimentation systems, or evaluation frameworks that informed release decisions.
• Exposure to applied AI, speech, conversational systems, customer-facing workflows, or other production ML domains.

Your role
As an Applied Scientist at Dialpad, you'll be a key driver within our AI team, conducting R&D to power the next generation of autonomous voice agents. While traditional NLP focuses on analyzing static text, your work will center on real-time, multimodal systems that can listen, reason, and take action during live customer interactions. A major focus of the team is advancing DialpadGPT, our proprietary LLM, specifically optimizing it 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 agentic applications.

If you're passionate about agentic and multimodal 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 Senior Manager of the NLP team and has the opportunity to be based in Vancouver, BC.

What you’ll do

• Research and develop state-of-the-art algorithms for autonomous voice agents, specifically focusing on real-time speech processing and reasoning loops.
• Advance DialpadGPT: Design and execute distributed training strategies to optimize our proprietary LLMs for agentic behaviors, including precise tool use, instruction following, and latency-constrained generation.
• Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts.
• Design and implement orchestration layers that effectively chain LLMs with external tools and APIs to solve complex customer problems autonomously.
• Work with large-scale multimodal datasets (text, audio) to improve model robustness and alignment.
• Collaborate with engineering, product, and design teams to deploy scalable, low-latency models and algorithms in production.
• Submit papers to top-tier academic conferences (ACL, EMNLP, NeurIPS) and contribute to the team’s research culture.

Skills you’ll bring

• Master’s or PhD degree in Computer Science, Machine Learning, Computational Linguistics, or a related quantitative field.
• 2+ years of industry experience in Machine Learning/NLP for Master’s degree holders, or 1+ years for PhD holders.
• Deep understanding of LLMs: Demonstrated experience with training, fine-tuning (PEFT/LoRA), and alignment techniques (RLHF/DPO) for specific domains or tasks.
• Experience with Agentic Systems: Familiarity with building autonomous agents, including concepts like tool use, function calling, reasoning chains (CoT), and memory management.
• Strong proficiency in Python and PyTorch, with the ability to write clean, production-ready research code.
• Research Track Record: A history of publishing in top-tier conferences (ACL, EMNLP, NeurIPS, ICASSP) is highly valued.
• Multimodal Awareness: Familiarity with speech technologies (ASR, TTS) or processing real-time audio streams is a strong plus.
• Ability to bridge the gap between research and product, translating complex technical concepts into business value.
• Familiarity with version control tools like Git for collaborative projects.

Your role
As an analytics engineer, you’ll be an integral part of our data analysis and QA team, providing essential data support for our cutting-edge Agentic AI initiatives, specifically with ASR and NLP teams. A key focus will be on leveraging data analysis to identify and drive optimization of the conversational flows and underlying data infrastructure for agentic voice and chat solutions. You will actively seek opportunities to improve the team’s productivity and workflow through automation and process optimization, including implementing automated QA for our agentic systems.

This position reports to the manager of the Data Analysis and QA team and has the opportunity to be based in our Vancouver, Canada Office.

What you’ll do 

• You will work closely with other Agentic AI teams to test and evaluate our Agentic voice and chat solutions.
• You will design and implement data logging and monitoring strategies to capture key analytical insights.
• You will utilize data analysis to identify areas for optimization in the voice bot's conversational flows and data pipeline.
• You will build and maintain data pipelines on Vertex AI pipelines. 
• You will work closely with the AI Platform team to build tooling for data science projects. 
• You will implement automation and processes to improve our workflow. 
• You will create and maintain dashboards (Tableau) and data pipelines (Dataform) that help drive product and business decisions.
• You will contribute to our continuous efforts to enforce data privacy and compliance. 
• You will collaborate with cross-functional teams, including AI, engineering, and product teams.

Skills you’ll bring 

• Bachelor's or Master’s degree in Computer Science, Software Engineering, or related fields.
• 1 - 3 years of working experience with software engineering or data engineering projects. 
• 1 - 3 years of experience with Python, working with GCP, including storage, BigQuery, Compute, Kubernetes, or similar.
• 1 - 3 years of experience with SQL, able to optimize complex SQL queries and build data pipelines. 
• Experience with BI tools such as Tableau. 
• Experience analyzing the performance of conversational AI systems (e.g., voice bots, chatbots) and collaborating with cross-functional teams (AI, Product) on data-driven improvements.
• Experience working with popular LLM frameworks.
• Strong problem-solving and analytical abilities, with the capacity to handle complex technical and analytical problems.