Develop robust AI/ML solutions for manufacturing, collaborating with scientists to build scalable infrastructure, optimizing ML systems, and ensuring code quality.
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.
As a Senior 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.
- 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.
Gauss Labs San Jose, California, USA Office
San Jose, CA, United States
Similar Jobs
Cloud • Information Technology • Security • Software • Cybersecurity
Drive automation-first reliability for a global, multi-cloud platform: build scalable infra (AWS/GCP/bare-metal), write automation (Python/Go), implement observability (Prometheus/Grafana/OpenTelemetry), lead incident response/on-call, define SLIs/SLOs, and partner on operability reviews and post-incident analysis.
Top Skills:
AnsibleAWSAzureBgpC/C++DnsGCPGoGrafanaGreHaproxyHelmIpsecItilLinuxOpentelemetryPrometheusPythonRhelTemporalTerraform
Cloud • Information Technology • Security • Software • Cybersecurity
Lead Cloudflare's investor relations and strategic finance work: own the quarterly earnings cycle, guidance, consensus discipline, investor engagement, Investor Day, modeling and validation, cross-functional coordination, and executive communications for public markets.
Aerospace • Hardware • Information Technology • Security • Software • Cybersecurity • Defense
Lead supplier quality for PWB/PCB suppliers: audit, evaluate, and improve manufacturing and inspection processes. Drive root-cause analysis, failure investigations, FAI/PPAP, source inspections, supplier scorecards, and continuous improvement to ensure first-time quality for aerospace and defense programs.
Top Skills:
ApqpAs9100As9102Asme Y14.5Black BeltControl PlanFaiGreen BeltIpc-6012Ipc-6013Ipc-6018Ipc-A-600Ipc-A-610Ipc-Tm-650Lean Six SigmaMil-Prf-31032Mil-Prf-38534Mil-Std-883PfmeaPpapSource Inspection
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
