Engineer, Machine Learning - Remote

Logistics at full potential.

At GXO, we're constantly looking for talented individuals at all levels who can deliver the caliber of service our company requires.

You know that a positive work environment creates happy employees, which boosts productivity and dedication.

On our team, you'll have the support to excel at work and the resources to build a career you can be proud of.

We're out to transform transportation logistics through technology, and our multimillion-dollar commitment to IT underscores its importance to our vision.

As a Machine Learning Engineer, you will be responsible for designing, building, and maintaining scalable machine learning systems.

You will work closely with data scientists, software engineers, and business stakeholders to deploy models into production, ensure system reliability, and optimize performance across GXO's operations.

Pay, benefits and more.

We are eager to attract the best, so we offer competitive compensation and a generous benefits package, including full health insurance (medical, dental and vision), 401(k), life insurance, disability and more.

What you'll do on a typical day:


* Deploy and monitor ML models in production | Use tools like Docker, Kubernetes, and MLflow to ensure scalable and reliable deployment


* Build and maintain data pipelines | Use tools like Airflow, Spark, or Kafka to support model training and inference


* Integrate ML models into business applications | Collaborate with software engineers to embed models into operational systems


* Monitor model performance and data drift | Implement alerting and retraining pipelines


* Clean and preprocess data | Ensure data quality and consistency for modeling


* Work with cross-functional teams | Translate business needs into technical solutions


* Maintain technical documentation | Ensure reproducibility and knowledge sharing


* Optimize ML workflows | Identify and implement improvements in performance and scalability

What you need to succeed at GXO:

At a minimum, you'll need:


* Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics or another analytics-related field, or equivalent related work or military experience


* 3-5 years in AI/ML engineering or Data Science, or software engineering with ML focus


* ML Engineering Expertise: Strong understanding of ML lifecycle, from training to deployment and monitoring.


* Programming Proficiency: Advanced Python skills; experience with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).


* MLOps Tools: Proficiency in Docker, Kubernetes, MLflow, and CI/CD pipelines.


* Data Engineering: Experience with data pipeline tools (Airflow, Spark, Kafka).


* Cloud Platforms: Familiarity with AWS, GCP, or Azure.

It'd be great if you also had:


* Collaboration: Ability to work with data scientists, developers, and business stakeholders.


* Communication: Ability to explain GenAI conce...