Senior Machine Learning Engineer - Mississauga, ON (Hybrid)

By combining state-of-the-art data science techniques, cutting-edge Internet of Things (IoT) technologies, and Software as a Service, we enable a more connected, intelligent and efficient supply chain. We’re creating value from massive, connected data. Our unmatched insights illuminate more than 300,000 supply chains, more than a million customers and partners, and over 300 million physical assets that are constantly on the move around the world.

CHEP is a Brambles / BXB Digital company, the global leader in supply chain logistic solutions operating through the CHEP brand. Brambles Limited is listed on the Australian Securities Exchange (ASX) and has its headquarters in Sydney, Australia. Operating in more than 60 countries, with its largest operations in North America and Western Europe, we employ more than 14,500 people and owns over 550 million pallets, crates and containers through a network of approximately 850 service centres.

SENIOR MACHINE LEARNING ENGINEER

POSITION PURPOSE

We are seeking a Senior Machine Learning Engineer to design, build, deploy, and operate scalable machine learning and AI solutions in production. This role sits at the intersection of MLOps, traditional data science modeling, and software engineering, with opportunities to work on AI/GenAI engineering use cases.

You will work closely with Data Scientists and Engineers to productionize ML and emerging GenAI solutions, owning the full lifecycle from model development through deployment, monitoring, and iteration.

SCOPE

• Machine Learning models for Advanced D&A Americas.
• Data products initiatives for Advanced D&A Americas.
• GenAI initiatives for Advanced D&A Americas.

MAJOR / KEY ACCOUNTABILITIES

• Build, maintain, and optimize end to end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, inference and monitoring using Databricks and related tooling.
• Collaborate closely with Data Scientists to translate experimental and research grade models into reliable, scalable, and secure production services that meet business and technical requirements.
• Apply MLOps best practices including model versioning, experiment tracking, monitoring, and automated deployments.
• Develop and deploy traditional ML models (e.g., regression, classification, forecasting, NLP) to solve business problems.
• Implement runtime monitoring dashboards and alerting mechanisms to detect performance degradation, data anomalies, and system failures in near real time.
• Support AI / GenAI initiatives, including LLM based prototypes and production workflows where applicable.
• Collaborate with product owners, data scientists, engineers, and business stakeholders to define model requirements, SLAs, success metrics, and deployment constraints.
• Integrate ML solutions into downstream systems via APIs, batch pipelines, or event driven processes.
• Write high quality, maintainable code following engineering best practices, with version control and CI/CD in Bitbucket.
• Troubleshoot and optimize model performance, scalability, latency, and cost in production environments.
• Provide guidance and best practices to data scientists and engineers on production ready ML development and MLOps workflows.
• Evaluate emerging tools, frameworks, and practices to enhance the organization’s ML and GenAI operational maturity.

MEASURES

• ML models are reliable, scalable, and observable in production environments
• Reduced time and friction moving from experimentation to production ML systems
• High availability and reliability of ML pipelines and inference services
• Strong collaboration with Data and cross functional teams resulting in business impacting ML solutions
• Clear observability into model performance, data quality, and system health
• Adoption of standardized patterns for ML development and deployment across the team

KEY CONTACTS

Internal: Data & Analytics Americas, Processes Digitalization, Supply Chain, Commercial, Serialization+, Finance, Digital

QUALIFICATIONS

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, Mathematics, or a related field, or 7+ years of equivalent professional experience in a related role
• Strong foundation in machine learning algorithms and applied modeling techniques
• Demonstrated ability to build and operate production grade software systems is a plus
• Proven ability to work in ambiguous problem spaces and evolving AI landscapes

EXPERIENCE

• 5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a closely related role
• Hands on experience deploying and supporting ML models in production
• Proven experience using ML lifecycle management tools such as MLflow (preferred) or similar platforms
• Experience using Databricks or similar platforms for data processing and ML workloads
• Proven collaboration with Data Scientists and Engineers in cross functional teams
• Experience supporting both early stage experimentation and production systems

SKILLS AND KNOWLEDGE

• Strong understanding of supervised and unsupervised learning techniques
• Feature engineering, model evaluation, and performance optimization
• Experience operationalizing models beyond notebooks
• Building and maintaining ML pipelines (training, inference, retraining)
• Model versioning, experiment tracking, and reproducibility
• Monitoring for model performance, data drift, and pipeline failures
• CI/CD practices for ML workflows
• Strong proficiency in Python
• Writing testable, maintainable, production quality code
• Git based version control workflows
• Experience integrating ML into applications or services
• Exposure to LLMs, embeddings, prompt engineering, or retrieval augmented generation (RAG)
• Experience moving GenAI use cases from prototype to production
• Familiarity with evaluating GenAI outputs and monitoring cost, latency, and quality
• Experience building or consuming REST APIs for model inference
• Understanding of distributed systems and data pipelines

The salary range for this position is $103,000 to $140,000 / year. Salary ranges provided take into account a wide variety of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications, geographic differentials and other business and organizational needs. Therefore, actual amounts offered may be higher or lower than the range provided. If you have questions, please speak to your Talent Acquisition Partner about the flexibility and detail of our compensation philosophy.” Dependent on the position offered, other forms of compensation may be part of a total offering beyond medical & retirement benefits and may include other monetary incentives or business benefits.