Senior Machine Learning Engineer

Klue Engineering is hiring!We're looking for a Senior Machine Learning Engineer to join our ML Foundation and Platform 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 ML and IR fundamentals who wants to dive deep into practical LLM applications. FAQQ: 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: As a member of our team, you'll be focusing on optimizing LLM-based agents, creating a product for other teams to utilize ML capabilities and deploying ML services to production. You'll measure and improve retrieval systems across the spectrum from BM25 to semantic search and develop comprehensive evaluation metrics to measure their performance. A key challenge will be developing optimal chunking and enrichment strategies for diverse data sources including news articles, website changes, documents, CRM entries, call recordings and internal communications. You'll explore how different data types and formats impact retrieval performance and develop strategies to maintain high relevance across all sources.Beyond agents and retrieval, you'll work on building a product for other teams to effectively utilize LLM tools and take advantage of prompt engineering.This includes developing APIs and scalable systems, developing scalable tools and services to handle machine learning training and inference for our clients, writing zero-shot and few-shot prompts with structured inputs/outputs, and implementing benchmarking systems for prompts. You'll also work on training and fine-tuning smaller, more efficient models that can match the performance of LLMs at a fraction of the cost. This includes creating labeled datasets (sometimes using prompts), conducting careful hyperparameter optimizations, and building automated training pipelines. You'll also deploy and monitor these models in production, optimize their latency, and implement comprehensive offline/online metrics to track their performance. Throughout all this work, you'll apply your deep understanding of the latest breakthroughs to build scalable, production-ready systems that turn cutting-edge ML experiments into reliable business value.Q: What experience are we looking for? Masters or PhD in Machine Learning, NLP, or related field2+ years building and optimizing retrieval systems2+ years of training/fine-tuning transformer modelsDeep understanding of LLMs, retrieval metrics, and their trade-offsImplement memory and tool-use strategies to enhance LLM-based agent capabilitiesExperience building end-to-end systems as an MLOps Engineer or Data EngineerStrong understanding of software testing, benchmarking, and continuous integrationBuild scalable, production-ready ML pipelines for training, evaluation, deployment,abhik and monitoringDevelop and implement CI/CD pipelines. Automate the deployment and monitoring of ML models.Knowledge of query augmentation and content enrichment strategiesExpertise in automated LLM evaluation, including LLM-as-judge methodologiesSkilled at prompt engineering - including zero-shot, few-shot, and chain-of-though.Proven ability to balance scientific rigor with driving business impactQ: What makes you thrive at Klue? A: We're looking for builders who:Take ownership and run with ambiguous problemsJump into new areas and rapidly learn what's needed to deliver solutionsBring scientific rigor while maintaining a pragmatic delivery focusSee unclear requirements as an opportunity to shape the solutionQ: What technologies do we use? LLM platforms: OpenAI, Anthropic, open-source modelsML frameworks: PyTorch, Transformers, spaCySearch/Vector DBs: Elasticsearch, Pinecone, PostgreSQLMLOps tools: Weights & Biases, MLflow, LangfuseInfrastructure: Docker, Kubernetes, GCPDevelopment: Python, Git, CI/CDQ: What is your working style at Klue?Hybrid. Best of both worlds (remote & in-office) You and your team will be in the office 2 days a week.Our main Canadian hubs are in Vancouver and Toronto, and most of our teams are located in EST and PST.Q: What about Compensation & Benefits: Competitive base salaryBenefits. Extended health & dental benefits that kick in Day 1Options. Opportunity to participate in our Employee Stock Option PlanTime 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⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️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) Culture, culture, culture! Winning as Women & More! About Us Product Demo Arena Glassdoor Youtube☕️ LinkedIn Wellfound (AngelList)

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