Senior Machine Learning Infrastructure Engineer
Key Skills for This Role
Full Job Posting
Responsibilities:
- 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.
Required Skills:
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
Salary Range:
$160,000 - $200,000 a year
About Plus-2
Private Physical AI company developing virtual driver software for factory-built autonomous trucks.
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