Senior AI Engineer - Machine Learning (US)

Gauss Labs; Emeryville (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Gauss Labs in Emeryville, California is seeking a passionate AI Engineer to develop cutting-edge AI solutions for the manufacturing sector. The role involves collaborating with AI Scientists, implementing machine learning systems, and optimizing performance.

Ideal candidates should have a strong background in programming, data science, and experience in building ML infrastructure. Join us in transforming manufacturing through AI!

Qualifications

• Strong expertise in Python for end-to-end model development.
• 3+ years building production-ready ML infrastructure.
• Solid understanding of software engineering best practices.

Responsibilities

• Collaborate with AI Scientists on model requirements.
• Build and maintain data processing infrastructure.
• Own end-to-end ML systems implementation.

Skills

Proficiency in Python, C++, or Java

Understanding of algorithms and data structures

Expertise in Python data science stack

ML/DL frameworks knowledge

Problem-solving skills

Education

BS in Computer Science or related field with 6 years experience

MA/PhD with 4 years experience

Tools

Docker

Kubernetes

Git

Job description

Gauss Labs is looking for a passionate and talented AI Engineer to develop 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.

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