Senior AI Engineer - Machine Learning (US)

Responsibilities

• 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.
• Work with product and engineering teams to understand requirements and integrate AI systems into user-facing applications.

Key Qualifications

• BS in Computer Science, Electrical Engineering, Machine Learning, or related technical field with 6 years of experience, or MA/PhD with 4 years of experience.
• 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.

Preferred Qualifications

• BS in Computer Science, Electrical Engineering, Machine Learning, or related technical field with 8 years of experience, or MA/PhD with 6 years of experience.
• 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.