Sr. Machine Learning Engineer

Enable International; Toronto; On-site; CAD 80,000 - 130,000; Full time

Job summary

Join an innovative SaaS company as a Senior Machine Learning Engineer, where you'll design and deploy cutting-edge AI solutions. This role offers the opportunity to work on retrieval-augmented generation systems and multi-agent architectures, pushing the boundaries of AI in rebate management. Collaborate with talented teams to deliver high-impact systems that shape the future of the platform while enhancing your own career growth. If you're passionate about machine learning and eager to tackle complex challenges, this is the perfect opportunity for you to make a significant impact in a fast-paced environment.

Qualifications

• 5+ years of experience in machine learning engineering or applied AI.
• Strong foundation in machine learning, data science, and Python proficiency.

Responsibilities

• Design, build, and deploy RAG systems and AI agent architectures.
• Collaborate with teams to optimize model pipelines for performance.

Skills

Machine Learning Engineering

Applied AI

Data Science Fundamentals

Python

Communication Skills

Education

Bachelor's or Master's in Computer Science

PhD in Computer Science or related field

Tools

PyTorch

Hugging Face Transformers

TensorFlow

FAISS

Pinecone

Weaviate

Azure

Docker

Kubernetes

MLflow

Job description

Do you want to help design new ways of processing Enterprise scale data at speed, learn leading edge technologies, work on complex big-data algorithms, shape processes into a growing engineering organisation, all while helping to scale a Series D rocket ship to the next level?

Then welcome to Enable.

What is Enable:

Enable is the SaaS rebate management platform that drives trusted relationships between B2B trading partners. We create money for our customers by providing them with the technology solutions to automatically detect and report on rebate due. Customers configure their deals, Enable ingests and process all their sales transactions, allowing them to find rebates they are owed that they would otherwise have missed.

All this has major challenges; we process enormous amounts of data in very short time frames, performing billions of calculations per customer and storing it all in Enterprise scale databases. We provide customers with reporting, deal editing and collaboration capabilities. There are no standard techniques for doing this; we are the market leader, and we create new solutions every day.

We launched our flagship product in 2016 and have raised $276m to date in Series A, B, C & D funding. We are continually growing our client base, product portfolio and hyper-talented team.

We’re hiring a Senior Machine Learning Engineer to join our AI and Architecture team, contributing to the design, development, and deployment of cutting-edge machine learning systems. In this role, you’ll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures, and AI agent workflows—into production.

As a Senior Machine Learning Engineer, you’ll play a key role in developing and integrating cutting-edge AI solutions—including LLMs and AI agents—into our products and operations at a leading SaaS company. You’ll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. This is a highly collaborative and fast-moving environment where your contributions will directly shape both the future of our platform and your own growth.

Key Responsibilities

• Design, build, and deploy RAG systems, including multi-agent and AI agent-based architectures for production use cases.
• Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation.
• Build evaluation pipelines to benchmark LLM performance and continuously monitor production accuracy and relevance.
• Work across the ML stack—from data preparation and model training to serving and observability—either independently or in collaboration with other specialists.
• Optimize model pipelines for latency, scalability, and cost-efficiency, and support real-time and batch inference needs.
• Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration.
• Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents, and evaluate their practical applicability.
• Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems.

Required Qualifications

• 5+ years of experience in machine learning engineering, applied AI, or related fields.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related technical discipline.
• Strong foundation in machine learning and data science fundamentals—including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering.
• Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments.
• Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow.
• Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex).
• Hands-on experience with fine-tuning and distillation of large language models.
• Comfortable with cloud platforms (Azure preferred), CI/CD tools, and containerization (Docker, Kubernetes).
• Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar.
• Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Engineering, or a related technical discipline.
• Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval.
• Familiarity with prompt engineering and building evaluation pipelines for generative models.
• Exposure to Snowflake or similar cloud data platforms.
• Broader data science experience, including forecasting, recommendation systems, or optimization models.
• Experience with streaming data pipelines, real-time inference, and distributed ML infrastructure.
• Contributions to open-source ML projects or research in applied AI/LLMs.
• Certifications in Azure, AWS, or GCP related to ML or data engineering.