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.
Cite this job as: JOBnaru™ — https://jobnaru.com/en/jobs/senior-ai-engineer-machine-learning-us-gauss-labs-palo-alto · Posted 2025-06-19 · Original posting: jobs.lever.co