Gauss Labs is looking for a passionate and talented AI Engineer for developing cutting-edge Industrial AI solutions that will normalize the standard of AI for manufacturing. We are working with the world's best manufacturing customers while accessing the vast amount of real data from their manufacturing processes. We apply state-of-the-art AI technologies to the data and develop unprecedented AI/ML solutions to transform manufacturing to the next level.As an AI Engineer, you will be responsible for translating cutting-edge AI and machine learning research into robust, scalable software solutions. Your work will ensure the seamless integration of models into production environments and contribute to the broader success of AI initiatives across the company. You will develop AI software by working with seasoned Applied Scientists, Software Engineers, and Program Managers located in Palo Alto, California, USA and Seoul, South Korea.
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.
Interface with product and engineering teams to understand requirements and drive integration of AI systems into user-facing applications.
Key Qualifications
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.
Preferred Qualifications
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.
