Backend Engineer, AI/ML

Backend Engineer, AI/ML – Klue Join Klue’s Engineering team in Toronto to build and optimize the retrieval infrastructure, APIs, and data pipelines that power our ML team’s agentic workflows, enabling fast, accurate, and scalable retrieval for advanced user‑facing systems. Base Pay Range CA$145,000.00/yr – CA$180,000.00/yr Responsibilities Design, implement, and maintain retrieval infrastructure and APIs that interface seamlessly with LLM‑based agent workflows. Integrate dense retrieval, hybrid retrieval, and re‑ranking models into live systems. Optimize latency, scalability, and throughput of retrieval systems for real‑time agentic pipelines. Build and maintain vector database infrastructure (FAISS, Milvus, Weaviate, Pinecone, PGVector) and traditional search engines (Elasticsearch, OpenSearch). Support retrieval‑augmented generation (RAG) workflows, including efficient query execution, chunk retrieval, and caching strategies. Develop monitoring and observability tools for retrieval pipelines to ensure reliability and transparency. Work with ML engineers on data pipelines for indexing and re‑indexing, enabling continuous improvement of search relevance. Contribute to the architecture of multi‑step retrieval agents, ensuring clean abstractions between the backend and ML layers. Qualifications 3+ years of backend engineering experience, ideally in search, retrieval systems, or high‑scale APIs. Strong programming skills in Python, Ruby, or similar backend languages. Experience with search infrastructure (Elasticsearch, OpenSearch, Vespa) and vector search systems. Understanding of retrieval pipelines, dense retrieval, and hybrid search. Familiarity with real‑time data pipelines (Kafka, Pub/Sub) for indexing workflows. Experience with distributed systems and microservices, focusing on reliability and performance. Familiarity with cloud infrastructure (AWS, GCP, Azure) and container orchestration (Kubernetes). Ability to work collaboratively with ML Engineers, understanding their experimentation workflows and constraints. Strong debugging and profiling skills for production systems. Nice to Have Experience with retrieval‑augmented generation (RAG) or agentic retrieval workflows. Exposure to prompt engineering and LLM system integration. Contributions to open‑source projects in search or retrieval. What Makes You Thrive at Klue 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. Technologies We Use 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. Working Style Hybrid: remote and in‑office. Team in the office 2 days a week. Main Canadian hubs in Vancouver and Toronto; teams in EST and PST. Compensation & Benefits Competitive base salary. Extended health & dental benefits that start Day 1. Opportunity to participate in Employee Stock Option Plan. Paid time off – average 2‑4 weeks per year. Direct access to leadership, including the CEO. EEO Statement At Klue, we’re dedicated to creating an inclusive, equitable, and diverse workplace as an equal‑opportunity employer. We welcome applicants of all backgrounds. 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. #J-18808-Ljbffr