Role overview
C.H. Robinson is seeking a Senior Machine Learning Engineer to help build robust, scalable systems that empower our data science and engineering teams.
As a Machine Learning Engineer, you will play a key role in delivering production‑grade, scalable ML infrastructure and services. You’ll collaborate directly with scientists and engineers across product teams to iterate on user needs, deliver intelligent capabilities, and optimize performance. You’ll also help build and maintain a reusable platform that accelerates the machine learning lifecycle: from experimentation to deployment and monitoring.
What you'll work on
The duties and responsibilities of this position consist of, but are not limited to, the following:
- Collaborate with data scientists, product engineers, and stakeholders to design, implement, and technically lead ML‑powered solutions
- Build and maintain reusable libraries, pipelines, and platform tools that enable efficient model training, deployment, and monitoring
- Contribute to evolving our domain models and ontology layer that supports consistent data and ML integration
- Iterate with users to ensure solutions are intuitive, performant, and aligned with their workflows
- Apply software engineering best practices, including secure coding, modular design, and CI/CD automation
- Write comprehensive unit and performance tests to ensure system stability and prevent regression
- Proactively identify and resolve technical debt, performance bottlenecks, and scalability challenges
- Document systems and code for maintainability and team‑wide knowledge sharing
- Stay current with industry best practices in MLOps, cloud computing, and ML system design
What we're looking for
- Demonstrated experience leading end‑to‑end delivery of production ML systems or platform components
- Experience implementing and scaling MLOps practices in a production environment
- Hands‑on experience working with large datasets and distributed training workflows
- Familiarity with feature stores, model registries, and experiment tracking tools
- Contributions to internal or open‑source ML libraries/platforms
- Ability to mentor junior engineers and influence engineering best practices
Tags & focus areas
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