We're looking for a 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'll be joining us at an exciting time as we reinvent our insight generation systems, making this an excellent opportunity for someone with strong Backend and ML fundamentals who wants to dive deep into practical LLM applications.
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?As a member of our team, you'll be leading the design and implementation of search and retrieval agent systems that enable users to discover high-quality, relevant information with minimal effort. You will work at the intersection of LLM-powered agent workflows, retrieval pipelines, and evaluation frameworks, ensuring that our systems remain scalable, efficient, and aligned with user intent.
On a day to day basis you will:Design and implement retrieval-augmented generation (RAG) systems with agentic workflows to refine query understanding, document retrieval, and response synthesis.
Build and optimize retrieval pipelines using BM25, dense retrieval, hybrid retrieval, and re-ranking approaches.
Develop evaluation pipelines for retrieval and generation, including offline metrics (recall, MRR, nDCG) and human-in-the-loop evaluations.
Experiment with query rewriting, expansion, and classification to improve retrieval relevance.
Collaborate closely with Product to bring ML-powered search agents into production.
Profile, debug, and optimize the latency, accuracy, and scalability of retrieval and generation components.
Contribute to the design of data pipelines for training retrieval and ranking models, including dataset curation, augmentation, and labeling workflows.
Stay up-to-date with advancements in LLMs, retrieval techniques, and agent architectures, evaluating opportunities to integrate them into our systems.
5+ years of software engineering experience
Experience with information retrieval systems, search relevance, and ranking models
Expertise in Python, with experience in frameworks such as PyTorch, TensorFlow, or JAX.
Familiarity with LLMs, prompt engineering, and retrieval-augmented generation pipelines.
Understanding of evaluation methods for search systems, including offline metrics and user-facing evaluation.
Experience working with vector database infrastructure (FAISS, Milvus, Weaviate, Pinecone, PGVector) and traditional search engines (Elasticsearch, OpenSearch)
Understanding of data pipelines, preprocessing, and large-scale data handling.
Ability to work independently and collaboratively in a fast-paced environment, balancing research and production needs.
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
Expertise in automated LLM evaluation, including LLM-as-judge methodologies
Skilled at prompt engineering - including zero-shot, few-shot, and chain-of-thought.
Experience with cloud infrastructure (AWS, GCP, Azure) for scalable ML workflows.
Nice to Have
Experience with agentic system design for LLM workflows.
Background in conversational search.
Contributions to open-source projects in the retrieval, NLP, or LLM ecosystems.
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
Yep - we're hybrid. Best of both worlds (remote & in-office)
Our main Canadian hubs are in Vancouver and Toronto. Ideally, this role would be located in Toronto.
You will be in office at least 2 days per week.
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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