Who you are

• 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, with a focus 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
• 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 the job involves

• We're looking for a Backend Engineer to join our team in Toronto to work on LLM-powered search and retrieval agents
• In this role, you will 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
• You will work closely with machine learning engineers, ensuring our backend systems can support rapid experimentation while remaining production-ready
• 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
• 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

Benefits

• We gather compensation benchmarking data across BC and Canada’s Tech Industry, and use that data to build a range for our current team and future talent based on skill, capabilities and potential
• We currently have extended health benefits
• Vacation. Unlimited Vacation. We want the team to prioritize wellness and avoid burnout. We want to also give individuals autonomy to choose how and when they take vacation. We understand and respect that everyone’s needs for time off are different, just like our team.
• Work-life integration: Hybrid work model, flexible working schedules, and a global team