AI for Distribution

Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 11-50H1B No SponsorCompany Site LinkedIn

Location

United States

Posted

151 days ago

Salary

Not specified

Bachelor DegreeEnglishPandasPythonPy TorchScikit LearnSparkTensorflow

Job Description

• Design, build, and maintain scalable machine learning models that improve and automate logistics processes for our customers. • Own projects end-to-end, from problem definition and data exploration to model deployment and monitoring in production. • Collaborate closely with engineering teams to align ML work with customer needs and deliver features that drive business value. • Serve as a technical leader and mentor within the ML area, reviewing code and ensuring best practices for reproducibility, quality, and performance. • Evaluate and implement tools and frameworks to improve our ML infrastructure and workflows. • Help shape the future of Canals as we continue scaling with our customers.

Job Requirements

Senior-level experience building and deploying machine learning models in production environments.
Experience designing scalable data pipelines and working with large datasets.
Comfort taking ownership of projects and ensuring models deliver real, measurable customer value.
Strong Python skills with knowledge of ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow) and data tools (e.g., Pandas, Spark).
Ability to guide and unblock others, providing thoughtful code reviews and architectural feedback.
Experience working independently in a fast-paced, product-focused environment.
Previous experience in high-growth startups or small teams is a plus.
Familiarity with MLOps practices and tools is a plus.

Benefits

Bootstrapped and profitable: stability without the chaos of venture pivots.
Real-world impact: your work improves global supply chains, saving customers time and reducing waste.
Strong engineering culture: we invest in quality and documentation to keep moving fast sustainably.
Remote-first, flexible work environment across North and South America.
Opportunity to grow while staying close to technical work, including potential for ML leadership as we scale.