Software Engineer - Data & ML Infrastructure Job Listing at AMD in Markham, ON (Job ID 74300-en-us)

Description

WHAT YOU DO AT AMD CHANGES EVERYTHING 

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.  Together, we advance your career.  

THE ROLE:

We are looking for an experienced Software Engineer with a strong interest in data infrastructure, automation and applied machine learning.

This role is critical to building AMD's next-generation data pipeline and analytics infrastructure to support AI workloads and GPU validation.

You will design and implement scalable, high-performance data systems that collect, process, and analyze GPU telemetry, performance, and power data.

You will work closely with firmware, software, and infrastructure teams to transform raw data into actionable insights and predictive intelligence.

THE PERSON:

You are passionate about software development with creative and effective problem-solving skills, a motivated, self-starter who can work both independently and collaboratively in fast paced environments.  You have excellent technical communication, interpersonal and leadership skills.

KEY RESPONSIBILITIES:

• Design, develop, and maintain scalable data pipelines for collecting, preprocessing, transforming, and storing large volumes of GPU and system telemetry data.
• Architect data models and ETL processes for both structured and unstructured data across SQL/NoSQL ecosystems.
• Integrate ML-based analytics (e.g., anomaly detection, performance prediction, power efficiency modeling) into production pipelines.
• Collaborate with multiple engineering teams to enable model training, evaluation, and deployment workflows using real-world GPU data.

REQUIRED SKILLS:

• Strong proficiency in Python with production-grade data pipeline experience.
• Solid understanding of databases (SQL & NoSQL) and distributed data systems (e.g., PostgreSQL, MongoDB, Kafka, or Databricks).
• Hands-on experience with ETL frameworks and orchestration tools (e.g., Airflow, Prefect, or Luigi).
• Familiarity with ML frameworks such as PyTorch, Scikit-learn, or TensorFlow for applied data analysis and predictive modeling.
• Experience with data visualization and reporting tools (e.g., Grafana, PowerBI) is a plus.
• Experience working with cloud-based storage and compute services e.g., Azure, AWS, or GCP.

PREFERRED EXPERIENCE:

• Background in hardware telemetry, performance, or GPU analytics.
• Experience building AI-driven automation systems or data-driven decision frameworks.
• Familiarity with containerized environments (Docker, Kubernetes) and CI/CD workflows.

ACADEMIC CREDENTIALS:

• Bachelor's/master's degree program in Computer Science, Engineering, Mathematics, Data Engineering or similar program with focus on Software Engineering.

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Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.