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# Machine Learning Infrastructure Engineer

- **Company:** Pallis Works, Inc.
- **Location:** Santa Clara, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, C++ (Programming Language), Computer Clusters, Databases, Continuous Integration, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Multiprocessing, Tensorflow, Standard Sql, Azure Machine Learning, Software Engineering, Data Logging, Pytorch, System Availability, Deep Learning, Convolutional Neural Networks, Containerization, Kubernetes, Information Technology, Machine Learning Operations, Software Version Control, Data Pipelines, Docker
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-infrastructure-engineer-plusai-8089719

## About the Role

* Phd or MS in Computer Science, Electrical Engineering, or related field
 * Good oral and written communication skills
 * Phd new grad or Masters with 3+ years of software engineering experience with a focus on ML infrastructure or distributed systems.
 * Proficiency in in Python, C++, SQL
 * Deep understanding of containerization, orchestration technologies, distributed ML workload, and experiment tracking tools (e.g., Docker, Kubernetes, multiprocessing, Kubeflow, and mlflow)
 * Deploy and manage resources across multiple cloud platforms (AWS, GCP, or on-prem environments)
 * Proficiency in at least one deep learning framework, such as PyTorch and data pipeline tools (e.g., Apache Airflow, Prefect).
 * Strong knowledge of distributed systems, databases, and storage solutions.
 * Extensive software design and development skills.
 * Ability to learn and adapt to new technologies and contribute in a productive environment.
Preferred Skills:

 * Familiarity with fundamental deep learning architectures, such as Convolutional Neural Networks (CNNs) and Transformer models
 * Experience in building large-scale ML datasets, MLOps pipelines, and distributed computing frameworks like Ray
 * Experience working with autonomous vehicles or robotics

## Description

As a Senior ML Infrastructure Engineer at Plus, you will design scalable architectures capable of handling petabytes of data while ensuring optimal performance for both training and inference phases. You will build robust pipelines for managing model versioning systems and experiment tracking frameworks, which are essential for maintaining reproducibility across experiments. Additionally, you will be responsible for managing large-scale GPU clusters. This role offers unparalleled opportunities-both technically and professionally-for individuals passionate about solving challenging problems using modern cloud-native technologies. Ideal candidates thrive in environments that leverage tools such as Docker containers orchestrated via Kubernetes clusters, seamlessly integrated with state-of-the-art deep learning frameworks like PyTorch or TensorFlow. If you are eager to push the boundaries of what's possible in machine learning infrastructure and contribute to cutting-edge solutions, this, * Design and develop scalable, high-performance systems for training, inference, deploying, and monitoring ML models at scale.

 * Build and maintain efficient data pipelines, model versioning systems, and experiment tracking frameworks.
 * Collaborate with cross-functional teams, including ML researchers and engineers, to identify bottlenecks and improve platform usability.
 * Implement distributed systems and storage solutions optimized for machine learning workloadsDrive improvements in CI/CD workflows for ML models and infrastructure.
 * Ensure high availability and reliability of the ML platform by implementing robust monitoring, logging, and alerting systems.
 * Stay current with industry trends and integrate relevant tools and frameworks to enhance the platform.
 * Mentor junior engineers and contribute to a culture of technical excellence
 * Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
 * Ensure team compliance with QMS, monitor quality, and drive process improvements.


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