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. 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.
Key Qualifications
- BS in Computer Science, Electrical Engineering, Machine Learning, or a related technical field, plus 6+ years of full-time experience; or MS/PhD plus 4+ years of full-time experience.
- Strong programming skills in Python with a solid grounding in algorithms and data structures, and deep proficiency with the Python data/ML stack (NumPy, Pandas, scikit-learn, and PyTorch or TensorFlow) for end-to-end model development.
- 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).
- Track record of shipping ML systems with real attention to scalability, performance, and reliability.
- Demonstrated technical leadership: owning the roadmap for multi-quarter, multi-team efforts, driving cross-team and cross-functional alignment, and mentoring other engineers.
Preferred Qualifications
- BS in Computer Science, Electrical Engineering, Machine Learning, or a related technical field, plus 8+ years of full-time experience; or MS/PhD plus 6+ years of full-time experience.
- Experience pretraining, fine-tuning, and serving foundation models and transformers of any modality (tabular, timeseries, language, vision, etc.) at scale.
- 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.
- Experience with models under real-world data challenges — distribution shift, limited or noisy labels, or continual learning.
- Development in a cloud environment (AWS, Azure, or GCP).
- Productive with AI-assisted / agentic coding tools (e.g. Claude Code, Copilot) and eager to push their limits — building workflows and agents that raise the team's velocity.
