Senior Machine Learning Engineer

Klue; Toronto; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

A leading tech company is seeking a Senior Machine Learning Engineer in Toronto to optimize LLM-powered agents and build robust ML tools. This role involves enhancing retrieval systems, developing APIs, and deploying production-ready ML pipelines, combining technical expertise with a collaborative spirit to drive innovation.

Benefits

Extended health & dental benefits

Employee Stock Option Plan

Flexible time off

Direct access to leadership

Qualifications

• 2+ years building and optimizing retrieval systems.
• Deep understanding of LLMs and retrieval metrics.
• Experience with CI/CD pipelines.

Responsibilities

• Optimize LLM-based agents and deploy ML services.
• Develop APIs and scalable ML tools.
• Conduct training and fine-tuning of models.

Skills

Machine Learning

NLP

Prompt Engineering

Software Testing

Continuous Integration

Education

Masters or PhD in Machine Learning, NLP, or related field

Tools

PyTorch

Transformers

spaCy

Elasticsearch

MLflow

Job description

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.

Responsibilities

As a member of our team, you'll be focusing on optimizing LLM-based agents , creating a platform 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 platform 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.

Experience Required

• Masters or PhD in Machine Learning, NLP, or related field
• 2+ years building and optimizing retrieval systems
• 2+ years training / fine-tuning transformer models
• Deep understanding of LLMs, retrieval metrics and their trade-offs
• Implement memory and tool-use strategies to enhance LLM-based agent capabilities
• Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer
• Strong understanding of software testing, benchmarking, and continuous integration
• Build scalable, production-ready ML pipelines for training, evaluation, deployment and monitoring
• Develop and implement CI / CD pipelines. Automate the deployment and monitoring of ML models.
• Knowledge of query augmentation and content enrichment strategies
• Expertise in automated LLM evaluation, including LLM-as-judge methodologies
• Skilled at prompt engineering - including zero-shot, few-shot, and chain-of-thought.
• Proven ability to balance scientific rigor with driving business impact

What Makes You Thrive at Klue?

We're looking for builders who :

• 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

• ML frameworks : PyTorch, Transformers, spaCy
• Search / Vector DBs : Elasticsearch, Pinecone, PostgreSQL
• MLOps tools : Weights & Biases, MLflow, Langfuse
• Development : Python, Git, CI / CD

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.
• Benefits : Extended health & dental benefits that kick in Day 1
• Options : Opportunity to participate in our Employee Stock Option Plan
• Time 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.

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