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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 position is an excellent fit!
Job Responsibility:
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
Requirements:
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
Nice to have:
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
What we offer:
Work, learn and grow in a highly future-oriented, innovative and dynamic field
Wide range of opportunities for personal and professional development
Catered free lunch, unlimited snacks and beverages
Highly competitive salary and benefits package, including 401(k) plan
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