Senior Backend Engineer, AI / ML

Klue; Toronto; Hybrid; CAD 80,000 - 130,000; Full time

Job summary

An innovative company is seeking a Senior Engineer to join their ML Foundation and Platform team in Toronto. This role focuses on building and optimizing cutting-edge LLM-powered agents that enhance automation and workflow efficiency. You'll lead the design of ML services, improve retrieval systems, and develop comprehensive evaluation metrics. With a commitment to inclusivity and diversity, this forward-thinking firm offers a collaborative environment where your contributions will directly impact the evolution of their insight generation systems. If you are passionate about machine learning and eager to make a difference, this opportunity is for you.

Benefits

Extended health & dental benefits

Employee Stock Option Plan

Flexible PTO

Direct access to leadership

Qualifications

• 5+ years of experience in software engineering with a focus on ML.
• Expertise in building scalable ML pipelines and retrieval systems.

Responsibilities

• Lead design and implementation of LLM-based agents and ML services.
• Collaborate across teams to develop APIs and scalable ML tools.

Skills

Python

Machine Learning

Software Engineering

Prompt Engineering

Continuous Integration

Data Pipelines

Benchmarking

Retrieval Systems

MLOps

Education

Bachelor's Degree in Computer Science or related field

Tools

CI/CD Tools

ML Frameworks

Job description

Klue Engineering is hiring!

We're looking for a Senior 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 Backend and ML fundamentals who wants to dive deep into practical LLM applications.

Responsibilities

As a member of our team, you'll be leading the design and implementation of 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. 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 will collaborate cross teams to identify LLM solution needs and shape the team’s technical roadmap.

You will be responsible for building machine learning services and data pipelines to automatically extract insights about competitors from both public and internal data sources. Every day, our services process millions of data points, including news articles, press releases, webpage changes, Slack posts, emails, reviews, CRM opportunities, and user actions. You will maintain and develop services that utilize a broad array of ML techniques, including classification, clustering, recommendation, summarization, prompt engineering, vector search, RAG and agentic workflows.

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

• Expertise in Python
• 5+ years of software engineering experience
• Proven experience leading large cross team initiatives
• 3+ years building and optimizing retrieval systems
• Deep understanding of LLMs, retrieval metrics and their trade-offs
• Experience implementing 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
• Track record of staying current with ML research and breakthrough papers

Work Environment

• Hybrid. Best of both worlds (remote & in-office)
• Our main Canadian hubs are in Vancouver and Toronto. Ideally, this role would be located in Toronto.
• You and your team will be in office at least 2 days per week.

Compensation & Benefits

• Competitive base salary
• 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.

We’re excited to meet you and in the meantime, get to know us: