Senior Machine Learning Engineer

About Klue

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.

Role Overview

Klue Engineering is hiring! 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.

Responsibilities

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.
• 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.

Data Processing: 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.

Qualifications

What experience are we looking for?

• 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 sum.

Job Details

• Salary: CA$175K - CA$210K
• Location Type: Hybrid
• Employment Type: FullTime