Senior – MLE – Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM Watsonx

Before you apply to a job, select your language preference from the options available at the top right of this page.

Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.

• Job Description: About Machine Learning Engineering at UPS Technology:

We’re the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done… our innovative culture demands “yes and how!” We are UPS. We are the United Problem Solvers.

Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.

• About this Role:

We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You’ll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS’s strategic goals.

You will contribute across the full ML lifecycle—from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You’ll also help shape our MLOps practices and mentor junior engineers.

• Key Responsibilities:

• Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
• Build and scale ML pipelines using

Vertex AI Pipelines, Kubeflow, Airflow

, and manage infra-as-code with

Terraform/Helm

.

• Implement

automated retraining, drift detection, and re-deployment

of ML models.

• Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
• Implement

model monitoring, observability, and alerting

across accuracy, latency, and cost.

• Integrate and manage

feature stores, knowledge graphs, and vector databases

for advanced ML/RAG use cases.

• Ensure pipelines are

secure, compliant, and cost-optimized

.

• Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
• Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
• Stay current with the latest ML research and apply new tools pragmatically to production systems.
• Collaborate with product managers, DS, and engineers to

translate business problems into reliable ML systems

.

• Required Qualifications:

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).

Experience

• 5+ years of experience in machine learning engineering,

MLOps, or large-scale AI/DS systems

.

• Strong foundations in data structures, algorithms, and distributed systems.

Proficient in Python

(scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.

• Hands-on

experience building and deploying ML models at scale

in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).

• Experience with

containerization

(Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).

• Familiarity

with CI/CD pipelines, infrastructure-as-code

(Terraform/Helm), and configuration management.

• Experience

with big data and streaming technologies

(Spark, Flink, Kafka, Hive, Hadoop).

• Practical exposure to

model observability

tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)

• Strong understanding of

statistical methods, ML algorithms, and deep learning architectures

.

Preferred

• Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
• Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
• Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
• Domain experience in Logistics, supply chain, or large-scale consumer platforms.

• Employee Type:

Permanent

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

• Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
• Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
• Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
• Domain experience in Logistics, supply chain, or large-scale consumer platforms.