Senior Software Engineer, AI

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 competitive 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.

FAQ
Q:
Klue who?
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.

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.

Q:
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, Tensor Flow, 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, Open Search).

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…