ML Infrastructure Engineer: Real-Time AI Pipelines & Cloud DL

Analog Devices, Inc.; San Jose (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Analog Devices, Inc. in San Jose is seeking an ML Infrastructure Engineer to build and optimize AI/ML systems, enabling real-time training, testing, and deployment.

You will design distributed cloud GPU training, implement end-to-end ML pipelines, and own build-release CI/CD processes across DL frameworks, while embracing fast-paced innovation.

Qualifications

• 3+ years of software engineering experience, including distributed systems and real-time streaming.
• Degree in Computer Science or a related technical field.
• Strong system-level programming skills (Python, shell scripting) and Linux familiarity.

Responsibilities

• Build, deliver, and optimize software systems to enable AI/ML solutions.
• Design and implement ML systems and workflows for real-time training, testing, and deployment.
• Develop distributed cloud GPU training approaches for deep learning model training and evaluation.
• Create end-to-end ML pipelines and integrate them into product workflows.
• Own the build-release CI/CD processes across DL frameworks (e.g., TensorFlow, PyTorch, JAX).
• Propose and deploy scalable DevOps solutions to support frequent releases while maintaining quality.
• Automate recurring tasks for quality control and performance regression testing.

Skills

Distributed systems

Python

Linux administration

CI/CD

DevOps

Education

Bachelor's degree in Computer Science or related technical field

Tools

Kubernetes

Terraform

AirFlow

Dagster

TorchServe

TensorFlow

PyTorch

Job description

We're looking for engineers who bring expertise across the AI space, including ML platform design, cloud hosted AI services, foundational AI models, LLMs, Edge AI, and cutting-edge AI research; our list of breakthrough products and technologies is growing at a rapid pace. Central AI is critical to ADI’s future and presents opportunities to select from a variety of project areas as you and our AI-driven business grow. Finally, we need our team to be versatile, willing to take risks, able lead projects quickly, and be enthusiastic about new technologies and solutions.

Location: San Jose, CA or Boston, MA

Responsibilities

• As a ML Infrastructure Engineer, you help build, deliver, and optimize software systems to enable AI/ML solutions.
• Design and implement machine learning systems and workflows to support real-time training, testing, and deployment of AI models.
• Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation.
• Build end-to-end machine learning pipelines and integrate them into product and business system workflows.
• Architect and own the build-release continuous integration processes of our deep learning software components that are built, tested, and released on various DL frameworks (Tensorflow, PyTorch, JAX, etc.)
• Propose, implement, and deploy efficient and scalable DevOps solutions to allow our fast-growing team to release software more frequently while maintaining high-quality and top performance.
• Automate away recurring tasks (DL algorithm accuracy and performance regression detection, designing and developing new quality control measures, e.g., code analysis) while employing and advancing best practices.

Qualifications

• 3+ years of experience in software engineering, including experience with distributed systems real-time streaming.
• Degree in Computer Science or a related technical field.
• Strong system level programming skills (Python, shell scripting, etc.) and familiarity with Linux system administration.
• Hands-on experience with infrastructure engineering, modern DevOps processes, CI/CD, and GitHub.
• Experience with ML frameworks (Pytorch, Tensorflow, etc.) and model distribution frameworks (TorchServe, etc.).
• Experience with developing, implementing, and optimizing container orchestration systems, such as Kubernetes.
• Ability to work with and manage cloud data technologies, such as Kafka, ElasticSearch, Terraform, AirFlow, or Dagster.
• Excellent debugging and optimization skills.
• Experience working on software teams and willingness to work in a fast-paced environment.

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.