AI Engineer - Machine Learning (US)
3 Days Old
Job Description
- Collaborate with AI Scientists to understand model requirements and design scalable, efficient ML pipelines.
- Build and maintain reliable, performant infrastructure for data processing, model training, evaluation, and deployment.
- Own the end-to-end implementation of ML systems from research prototypes to production-grade code.
- Optimize model training/inference, latency, and resource usage to meet performance and system constraints.
- Develop monitoring, observability, and CI/CD tooling to support the full ML lifecycle in staging and production environments.
- Ensure engineering best practices in code quality, testing, documentation, and software reliability.
- Interface with product and engineering teams to understand requirements and drive integration of AI systems into user-facing applications.
- BS in Computer Science, Electrical Engineering, Machine Learning, or related technical field.
- Proficiency in one or more modern programming languages such as Python, C++, or Java with an understanding of algorithms and data structures.
- Strong expertise in Python data science stack (NumPy, Pandas) and ML/DL frameworks (scikit-learn, PyTorch, TensorFlow) for end-to-end model development.
- 3+ years building production-ready ML infrastructure, including data pipelines, training/inference workflows, and deployment automation.
- Solid understanding of software engineering best practices: version control (Git), unit testing, code review, and CI/CD. Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Experience developing software applications and services with an understanding of design for scalability, performance, and reliability.
- Strong problem-solving skills, attention to detail, and a collaborative mindset when working with research and product teams.
- MS or Ph.D. in Computer Science, Electrical Engineering, Machine Learning or related technical field.
- Knowledge of professional software engineering practices including source control management, code reviews, testing, and continuous integration/deployment.
- Experience in optimizing training and inferencing structures for large scale ML/DL models.
- Experience deploying machine learning models into production environments (e.g., batch, real-time, or edge deployments).
- Experience in distributed/parallel systems, information retrieval, networking, and systems software development.
- Development experience in a cloud service environment such as Amazon AWS, MS Azure, or Google Cloud Platform.
- Location:
- Palo Alto
- Category:
- Technology
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