AI Engineer - Machine Learning (US) at Gauss Labs

Palo Alto, California, United States

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Not SpecifiedCompensation

Junior (1 to 2 years), Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

ManufacturingIndustries

Requirements

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

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

Skills

Key technologies and capabilities for this role

PythonC++Machine LearningML PipelinesModel TrainingModel DeploymentModel EvaluationData ProcessingCI/CDMonitoringObservability

Questions & Answers

Common questions about this position

What is the salary for this AI Engineer position?

This information is not specified in the job description.

Is this AI Engineer role remote or office-based?

This information is not specified in the job description.

What skills are required for the AI Engineer role?

Required skills include proficiency in Python, C++, or Java; strong expertise in Python data science stack (NumPy, Pandas) and ML/DL frameworks (scikit-learn, PyTorch, TensorFlow); 3+ years building production-ready ML infrastructure; and solid understanding of software engineering best practices like Git, unit testing, CI/CD, Docker, and Kubernetes.

What is the team structure like for this position?

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.

What makes a strong candidate for this AI Engineer role?

A strong candidate has a BS in Computer Science, Electrical Engineering, Machine Learning, or related field, 3+ years of production ML infrastructure experience, strong Python and ML framework expertise, and a collaborative mindset; an MS or PhD and knowledge of professional software engineering practices are preferred.

Gauss Labs

AI solutions for semiconductor manufacturing efficiency

About Gauss Labs

Gauss Labs develops AI solutions specifically for the semiconductor manufacturing industry. Their technology focuses on process monitoring and optimization, utilizing machine-generated data to enhance factory intelligence and efficiency. By improving production quality and reducing downtime, Gauss Labs helps semiconductor manufacturers operate more effectively. Unlike many competitors, Gauss Labs tailors its AI infrastructure and software products to meet the unique needs of the manufacturing sector. The company's goal is to transform manufacturing processes through advanced AI, making factories smarter and more efficient.

California City, CaliforniaHeadquarters

2020Year Founded

$53.5MTotal Funding

LATE_VCCompany Stage

Industrial & Manufacturing, AI & Machine LearningIndustries

51-200Employees

Benefits

Competitive compensation + meaningful equity

Generous PTO policy

Comprehensive wellbeing benefits

Paid family leaves

L&D opportunities

Additional benefits unique to each site

Risks

Increased competition from established AI companies like Siemens and GE.

Potential over-reliance on SK hynix as a major client.

Rapid technological advancements could lead to obsolescence if innovation lags.

Differentiation

Gauss Labs specializes in AI solutions for semiconductor manufacturing, a niche market.

Their Panoptes VM 2.0 offers real-time, all-wafer measurement data, enhancing process efficiency.

The leadership team has deep expertise in AI, with experience from companies like Skytree.

Upsides

AI-driven predictive maintenance is gaining traction, aligning with Gauss Labs' focus.

AI-based virtual metrology adoption is increasing, benefiting Gauss Labs' Panoptes VM.

Demand for AI solutions in semiconductor manufacturing is expected to grow at 40% CAGR.

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