We're looking for a Senior Machine Learning Engineer to join our team in Toronto, focusing on building and optimizing state-of-the-art LLM-powered agents that can reason, plan and automate workflows for users. You will be leading the design and development of search and retrieval agent systems that enable users to generate compete insights for their business. In this role, you will own projects end-to-end, guiding architecture decisions, experimentation strategy, and production readiness for LLM-powered retrieval and generation workflows.
A: Klue is a VC-backed, capital-efficient growing SaaS company. Tiger Global and Salesforce Ventures led our US$62m Series B in the fall of 2021. We’re creating the category of competitive enablement: helping companies understand their market and outmaneuver their competition. We benefit from having an experienced leadership team working alongside several hundred risk-taking builders who elevate every day.
We’re one of Canada’s Most Admired Corporate Cultures by Waterstone HC, a Deloitte Technology Fast 50 & Fast 500 winner, and recipient of both the Startup of the Year and Tech Culture of the Year awards at the Technology Impact Awards.
Q: What are the responsibilities, and how will I spend my time?A: You will shape how we integrate retrieval-augmented generation (RAG), dense retrieval, query understanding, and agentic reasoning loops to deliver fast, accurate, and trusted search experiences at scale.
What you’ll do on a Day to day basis:Architect, design, and implement retrieval pipelines and agentic workflows, including hybrid retrieval, re-ranking, and post-retrieval synthesis.
Lead the development of evaluation frameworks (offline and human-in-the-loop) to measure and improve relevance, quality, and latency.
Drive experimentation with query rewriting, expansion, and classification to enhance retrieval effectiveness.
Optimize LLM workflows by designing prompt structures, retrieval strategies, and caching for low-latency, high-accuracy responses.
Collaborate cross-functionally with product and infrastructure teams to align technical direction with product goals.
Mentor and provide technical guidance to team members, establishing best practices for building production-ready ML systems.
Every day, our services process millions of data points, including news articles, press releases, webpage changes, Slack posts, emails, reviews, CRM opportunities, and user actions. You will own data strategy for retrieval and design pipelines to automatically extract insights about competitors from both public and internal data sources
Evaluate and integrate advancements in LLMs, retrieval architectures, and agentic reasoning into our production systems.
5+ years of industry experience building and deploying ML systems, with at least 2+ years working on search, retrieval, or ranking systems.
Expert-level programming skills in Python, with experience using frameworks such as PyTorch, TensorFlow, or JAX.
Deep understanding of information retrieval (BM25, dense retrieval, hybrid retrieval) and relevance tuning.
Experience with LLMs, retrieval-augmented generation pipelines, and prompt engineering.
Track record of designing and delivering production-grade ML systems at scale, balancing experimentation with reliability.
Deep understanding of data pipelines, preprocessing, and large-scale data handling.
Familiarity with evaluation methodologies for search systems (recall, MRR, nDCG) and user-facing evaluations.
Experience working with vector database infrastructure (FAISS, Milvus, Weaviate, Pinecone, PGVector) and traditional search engines (Elasticsearch, OpenSearch)
Familiarity with scalable cloud ML infrastructure (AWS, GCP, Azure).
Develop and implement CI/CD pipelines. Automate the deployment and monitoring of ML models.
Knowledge of query understanding, document summarization and other content enrichment strategies
Ability to lead projects independently while providing technical direction to others.
Nice to Have
Experience designing agentic LLM systems and multi-step retrieval workflows.
Background in conversational search
Contributions to open-source search, retrieval, or LLM-related projects.
Interest in publishing or sharing learnings with the broader community.
A: We're looking for builders who:
Take ownership and run with ambiguous problems
Jump into new areas and rapidly learn what's needed to deliver solutions
Bring scientific rigor while maintaining a pragmatic delivery focus
See unclear requirements as an opportunity to shape the solution
LLM platforms: OpenAI, Anthropic, open-source models
ML frameworks: PyTorch, Transformers, spaCy
Search/Vector DBs: Elasticsearch, Pinecone, PostgreSQL
MLOps tools: Weights & Biases, MLflow, Langfuse
Infrastructure: Docker, Kubernetes, GCP
Development: Python, Git, CI/CD
Hybrid. Best of both worlds (remote & in-office) You and your team will be in the office 2 days a week.
Our main Canadian hubs are in Vancouver and Toronto, and most of our teams are located in EST and PST.
Competitive base salary
Benefits. Extended health & dental benefits that kick in Day 1
Options. Opportunity to participate in our Employee Stock Option Plan
Time off. Take what you need. Just ensure the required work gets done and clear it with your team in advance. The average Klue team member takes 2-4 weeks of PTO per year.
Direct access to our leadership team, including our CEO
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Not ticking every box? That’s okay. We take potential into consideration. An equivalent combination of education and experience may be accepted in lieu of the specifics listed above. If you know you have what it takes, even if that’s different from what we’ve described, be sure to explain why in your application.
At Klue, we're dedicated to creating an inclusive, equitable and diverse workplace as an equal-opportunity employer. Our commitment is to build a high-performing team where people feel a strong sense of belonging, can be their authentic selves, and are able to reach their full potential. If there’s anything we can do to make our hiring process more accessible or to better support you, please let us know, we’re happy to accommodate.
We’re excited to meet you and in the meantime, get to know us:
Pay Up For Progress & 50 - 30 Challenge
✅✅ Win-Loss Acquisition (2023)
Series B (2021)
☕️ LinkedIn
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