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# Machine Learning Infrastructure Engineer

- **Company:** AllSTEM Connections
- **Location:** Ontario, CA, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Information Engineering, Distributed Computing Environment, Distributed Systems, Machine Learning, Build Management, Kubernetes, Machine Learning Operations, Data Pipelines
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/37bcc396-4fb8-4ea7-87ae-8f410fe2c2a6

## About the Role

Experience Baseline: Minimum of five (5) to ten (10) years of experience in designing, building, and maintaining large-scale ML infrastructure or distributed systems.
Infrastructure Mastery: Deep expertise in container orchestration (e.g., Kubernetes), distributed training, and cloud-native infrastructure.
Pipeline Expertise: Proven track record of managing massive-scale data pipelines and feature stores.
Collaboration: Strong "bridge-builder" personality-you are comfortable working in a high-velocity environment alongside both pure research scientists and core software engineers.

Preferred Attributes
Proven background at large-scale AI/ML-driven technology companies.
Experience with foundational model training infrastructure and techniques (e.g., model distillation, fine-tuning at scale).
A "Scalability Mindset"-you prioritize modular, reusable, and testable code over "quick and dirty" scripts.

## Description

ML Infrastructure & Automation
Workflow Architecture: Design and build end-to-end ML workflows and automated pipelines that minimize manual intervention and accelerate the path from experimentation to production.
Training & Serving Platforms: Architect and scale our distributed training and model-serving infrastructure. Build the platforms that handle foundational model training, knowledge distillation, and high-performance inference.
Data Engineering at Scale: Develop robust data sampling and feature generation platforms that provide high-quality input for our ML systems.
Automation & Reliability: Build foundational tools that standardize how we train, track, and deploy models, ensuring high platform reliability and minimal deployment drift.

Performance & Optimization
Cost & Efficiency: Drive architectural decisions that optimize our infrastructure footprint. Implement smart resource management and cost-optimization strategies for large-scale training clusters.
Developer Productivity: Build "developer-first" internal tools that reduce the cognitive load on researchers, allowing them to focus on model logic rather than infrastructure configuration.


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
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- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)