Senior Remote Machine Learning Engineer

CHEP helps move more goods to more people, in more places than any other organization on earth via our 347 million pallets, crates and containers. Through our pioneering and sustainable share-and-reuse business model, the world's biggest brands trust us to help them transport their goods more efficiently, safely and with less environmental impact.
You'll join an international organization big enough to take you anywhere, and small enough to get you there sooner. You'll help change how goods get to market and contribute to global sustainability. Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms.
Drive continuous integration and deployment of data science solutions, optimizing performance through advanced machine learning techniques, code reviews, and best practices.
'Develop and deliver sophisticated visualizations, dashboards, and reports translate complex data into clear, actionable insights for business stakeholders.
Present technical solutions to business stakeholders, using creative methods to explain complex concepts, increase understanding, and encourage solution adoption.
Mentor and develop junior data scientists, fostering a culture of continuous learning, knowledge sharing, and skills development within the organization.
Write clean, high-quality code, ensuring all outputs pass quality assurance checks, and contribute to the development of novel solutions to solve complex business problems.
Stay informed on industry trends, emerging tools, and techniques, applying them to improve data science practices and encourage innovation within the team.
Lead strategy development for one or more data products, managing roadmaps, identifying requirements, and collaborating with business stakeholders to ensure alignment with business goals.

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.

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

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

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

Bachelor's or master's degree in computer science, Engineering, Data Science, Mathematics, or a related field, or 3+ 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

3+ 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

Strong understanding of supervised and unsupervised learning techniques
Feature engineering, model evaluation, and performance optimization
Building and maintaining ML pipelines (training, inference, retraining)
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
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

Remote Type
Hybrid Remote
Adaptability, Bitbucket, Cloud Infrastructure (Aws), Code Reviews, Databricks Platform, Data Science, Data Storytelling, Empathy, Experimentation, Git, Machine Learning (ML), Python (Programming Language), SQL Tools, Taking Ownership, Teamwork, Understand Customers
This means harnessing the unique skills and experience that each individual brings and we do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state, or local protected class.

Brambles and CHEP never conduct interviews via online chat or request money as a term of employment.