Gauss Labs
Senior AI Engineer - Machine Learning (US)
Palo Alto, CA · Senior · Full-time
Sponsorship not specifiedDetected 399 days ago
PythonNode.jsData StructuresAlgorithmsCode ReviewGitAWSGCPAzureCloud PlatformsDockerKubernetesCI/CDMachine LearningTensorFlowPyTorchscikit-learnPandasNumPyData EngineeringNLPElectrical EngineeringResearchLeadership
About the role
- You'll work with AI Scientists, Software Engineers, and Program Managers across Palo Alto, CA, and Seoul, South Korea.
Responsibilities
- Partner with AI Scientists to build and productionize our tabular foundation models - owning the pretraining, fine-tuning, serving, and monitoring infrastructure and scaling it from research prototype to large-scale production.
- Build reliable, performant ML infrastructure across research, staging, and production: data, training, and inference pipelines, CI/CD, observability, and reproducible workflows, tuned for latency, throughput, and resource usage.
- Design evaluation and monitoring that reflect how models are actually used, including handling real-world data challenges such as distribution shift and limited labels.
- Set engineering standards, lead design and architecture reviews, and drive adoption of new modeling approaches, algorithms, and infrastructure.
- Partner with product and engineering teams to integrate ML into user-facing systems.
- Define scope and roadmap for multi-team initiatives, drive cross-team and cross-functional alignment across AI Science, engineering, and product, and mentor senior engineers to raise the organization's technical bar.
- 3+ years building production-grade ML infrastructure - data pipelines, training/inference workflows, and deployment automation - with solid software engineering fundamentals (Git, testing, code review, CI/CD, and containerization/orchestration such as Docker and Kubernetes).
- Gauss Labs builds Industrial AI for the world's leading manufacturers, applying state-of-the-art ML to large volumes of real production data.
- A core focus of this role is building our internal tabular foundation models - pretraining, fine-tuning, and serving them in large-scale production systems.
- As a Senior/Staff AI Engineer, you will turn ML research into robust, scalable production systems and own them across their full lifecycle.
Nice to have
- or MS/PhD plus 6+ years of full-time experience.
- Experience optimizing training and inference for large-scale models, including distributed/parallel training (multi-GPU/multi-node).
- Experience deploying ML in production across batch, real-time, or edge settings.
- Development in a cloud environment (AWS, Azure, or GCP).
- or MS/PhD plus 4+ years of full-time experience.
- Track record of shipping ML systems with real attention to scalability, performance, and reliability.
Benefits
- BS in Computer Science, Electrical Engineering, Machine Learning, or a related technical field, plus 6+ years of full-time experience
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This listing is sourced directly from Gauss Labs's careers page and normalized into a canonical job model.