Senior Engineer, Machine Learning - Remote (East Coast)

GXO Logistics

5d•Remote

About The Position

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. Please note role is for Eastern Time Zone location 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.

Requirements

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 of experience in software engineering, ML engineering, or data science, with at least 2 years focused on designing, building, and deploying scalable, production-grade machine learning systems.
Strong proficiency in cloud environments (GCP preferred; AWS and Azure acceptable).
Expertise in Python, APIs, SQL, system design, DevOps/MLOps, and familiarity with distributed systems.
Familiarity with common ML frameworks such as TensorFlow, scikit-learn, PyTorch, and related tooling.
Experience in monitoring, troubleshooting, and optimizing deployed solutions.
Strong analytical and problem-solving skills.
Strong understanding of all stages of the ML lifecycle.

Nice To Haves

Familiarity with logistics systems and supply chain systems (e.g., WMS, OMS, TMS).
Experience with Snowflake and its ecosystem.
Hands-on experience with GCP Vertex AI.
Experience with JavaScript and integrating ML outputs in user-facing applications.

Responsibilities

Design, develop, deploy, and maintain ML systems, microservices, and software components that enhance and support supply chain operations and technology.
Ensure software engineering, DevOps, and cybersecurity best practices in development and deployment, including CI/CD pipelines, source control, and secure coding standards.
Design and build, and/or collaborate with data engineering teams to develop data models, pipelines, and integration layers that support and feed ML solutions, ensuring scalability, reliability, and data quality.
Develop ML integration APIs and services using Python, SQL, and frameworks such as Flask and FastAPI with a focus on reliability, latency, and maintainability.
Build agile and portable ML solutions using containerization tools like Docker and Kubernetes.
Implement monitoring, alerting, and observability for ML services.
Collaborate with business and product stakeholders to understand use cases and educate teams on ML capabilities.
Work closely with IT teams (infrastructure, InfoSec, data engineering) to define internal requirements and ensure seamless integration.
Communicate effectively with leadership to secure resources, address issues, and provide project updates.
Take ownership of projects end-to-end with minimal supervision.
Mentor junior engineers on best practices in ML and software development through pair programming, code reviews, and architectural guidance.
Stay current on emerging ML and platform technologies and contribute to the organization’s ML roadmap.
Maintain high-quality technical documentation across systems, services, pipelines, and deployment workflows.