Senior Machine Learning Infrastructure Engineer

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…