Intermediate Machine Learning Engineer
BEES Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at BEES. Campinas.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Extend the ML platform for model training, inference, and both batch and online delivery
- Build reusable workflow components for organizing projects, experiments, model versions, and reproducible development
- Set up monitoring, logs, and alerts to track model performance, quality, and drift in training and production
- Tune model deployments, including scaling and resource use, to meet cost, quality, and SLA goals
- Resolve pipeline and serving problems, record the fixes, and share what you learn with teammates
What you bring
- A bachelor's degree in computer science, engineering, mathematics, or another quantitative discipline
- Hands-on work with ML platform elements such as feature pipelines, model registries, or training and inference workflows
- Strong software engineering practices, including clean code, testing, CI/CD, and maintainable system design
- Working ability in Python, PySpark, and SQL
- Experience with the listed cloud, infrastructure, data, and ML tools, including Azure Cloud, Kubernetes, Databricks, and ML frameworks
Who this fits
This mid-level role suits an engineer who can collaborate across teams, explain technical tradeoffs, and learn from senior engineers on architecture and platform choices. The role is based in Campinas, Brazil.
From the employer
About BEES
Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!
What you'll do:
- Implement and extend ML platform capabilities (training jobs, inference services, batch and online serving) following team architecture, standards, and best practices.
- Develop and maintain components of the ML model development workflow (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
- Build and operate observability for models in training and production—monitoring, logging, and alerting for performance, quality, and drift—in collaboration with platform and SRE partners.
- Support optimized model deployments (scaling, resource allocation, inference tuning) to meet cost, quality, and SLA targets.
- Troubleshoot pipeline and serving issues, document solutions, and share learnings with the team.
What you'll need:
- Bachelor’s degree in computer science, engineering, mathematics, or another quantitative field.
- Practical experience with ML platform components (e.g., feature pipelines, model registries, training and inference workflows).
- Solid software engineering fundamentals: clean code, testing, CI/CD, and maintainable system design.
- Python, PySpark, and SQL. Exposure to Java is a plus.
- Experience with Kubernetes, Databricks, Terraform, Azure DevOps (Git), Azure Cloud, and ML frameworks/libraries such as Scikit-learn, PyTorch, TensorFlow, ONNX, and serving tools (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.).
- Comfort collaborating across teams, communicating technical tradeoffs clearly, and learning from senior engineers on architecture and platform decisions.
What We Offer:
- Performance based bonus*
- Attendance Bonus*
- Private pension plan
- Meal Allowance
- Casual office and dress code
- Days off*
- Health, dental, and life insurance
- Medicines discounts
- WellHub partnership
- Childcare subsidies
- Discounts on Ambev products*
- Clube Ben partnership
- Scholarship*
- School materials assurance
- Language and training platforms
- Transport allowance
*Rules applied