# Senior AI Engineer at Guidepoint > Hybrid · Canada · Senior · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Aguidepoint%3A8859975002 (read 2026-11-04). ## Facts (observed) - Company: [Guidepoint](https://alderhq.app/companies/guidepoint) - Location text: Toronto, Ontario, Canada; Toronto - Where it can be done from (inferred): Hybrid · Canada (evidence: "Location: Toronto, Ontario, Canada; Toronto") - Posted: 2026-10-05 (30 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/guidepoint/jobs/8859975002 - Skills listed (inferred): TypeScript, Python, Azure, Kubernetes, Docker, LLMs ## Pay and sponsorship - Posted pay: CAD 175k–CAD 210k per year (from the listing's pay field) - Market band for similar posted roles (Toronto, n 9): p25 CAD 144k, median CAD 193k, p75 CAD 196k ## Description (the employer's text) ### Overview Guidepoint is investing in applied AI across its research platform. We're building conversational AI systems that interact with our global expert network at scale — combining real-time voice, LLM-driven orchestration, and retrieval over large proprietary datasets. You'll be an early engineer on a new initiative: taking working prototypes to production and shaping the architecture as the product scope grows. This is a hands-on role with broad ownership, close to product and leadership. ### This role is hybrid in our Toronto Office ### What You'll Do - Real-time conversational AI: voice- and chat-based agents with low-latency requirements, built on modern telephony and real-time media infrastructure. - LLM orchestration: agentic systems that pursue goals over multi-turn conversations, with hard behavioral guarantees enforced in code — not just prompts. - Multi-channel session management: stateful user interactions that move seamlessly across channels. - Retrieval and grounding: connecting LLM systems to large internal knowledge assets, with structured data flowing back. - Evaluation infrastructure: measuring conversational quality against human baselines and gating rollouts on metrics, not vibes. - Own services end-to-end: design, implementation, deployment (Kubernetes/Azure), and operations, including latency-sensitive real-time workloads. - Design and tune LLM pipelines: context engineering, structured outputs, model selection, cost/latency tradeoffs across vendors. - Build the eval loop and treat quality as a number: define metrics, run experiments, gate releases on them. - Integrate third-party AI and voice vendors behind clean abstractions so providers stay swappable. - Partner with product and compliance stakeholders to encode domain rules into the system. - Set engineering patterns for a growing team: code review, observability, incident habits. ### What You Have - 6+ years of software engineering, with 2+ years building LLM-powered products in production (not notebooks): orchestration, RAG/grounding, structured outputs, eval-driven iteration. - A Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience; or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI. This must include a proven track record of designing, building, and scaling distributed, production-grade system - Strong TypeScript or Python; comfortable working across both. - Real-time or streaming systems experience: WebSockets, WebRTC, audio pipelines, or comparable low-latency work. - Cloud-native production experience: containers, Kubernetes, CI/CD, observability. Azure a plus. - Familiarity with agent/LLM frameworks such as LangGraph, LangChain, or CrewAI — hands-on exposure is enough; we care that you know the landscape and when plain code beats a framework. - Working familiarity with vector databases and embedding-based retrieval. - Pragmatic product judgment: you scope prototypes, kill weak approaches with data, and ship. ### Nice-to-have - Voice AI: telephony platforms, conversational agent frameworks, STT/TTS, turn-taking/VAD. - Knowledge graphs or large-scale search/retrieval systems. - Regulated or compliance-sensitive domains (healthcare, finance). - Eval harnesses or LLM-as-judge systems. What We Offer: The annual base salary range for this position is 175,000-210,000 CAD. Additionally, this position is eligible for an annual discretionary bonus based on performance. You will also be eligible for the following benefits: - Paid time off - Comprehensive benefits plan - Company RRSP match - Development opportunities through the LinkedIn Learning platform About Guidepoint: Guidepoint powers end-to-end research workflows for the world’s best research teams. Backed by a global network of more than 2 million experts and over 1,600 employees, Guidepoint delivers real-time access to expertise, primary research, and actionable knowledge that help organizations make informed decisions. Through consultations, surveys, events, proprietary content, and AI-enabled tools, we support every stage of the decision-making process—from developing hypotheses and gathering insights to validating assumptions and building conviction for critical decisions. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aguidepoint%3A8859975002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.