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Remote Infrastructure Engineer Manager Jobs in California

Nvidia

Senior Compiler Engineer Infrastructure

Santa Clara, CA · On-site +1

$127K - $173K/yr

... management, validation, and large-scale refactoring Explore and apply AI-assisted tools to improve developer workflows, including code navigation, change analysis, refactoring assistance, testing ...

Nvidia

Senior Compiler Engineer Infrastructure

Santa Clara, CA · On-site +1

$127K - $173K/yr

Design and build infrastructure, tooling, and developer workflows that improve productivity ... Develop automation and developer tools to aid in rebasing, patch management, validation, and large ...

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Senior Machine Learning Infrastructure Engineer

Santa Clara, CA • On-site, Remote

PlusAI
See more jobs at PlusAIexternal

$160K - $200K/yr

Full-time

Retirement

Re-posted 28 days ago

Key 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 to identify bottlenecks and improve platform usability.

Job description

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

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!
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
Our compensations (cash and equity) are determined based on the position, your location, qualifications, and experience.

Your opportunities joining PlusAI
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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.