3 months ago
Palo Alto, CA, USASenior
Responsibilities
- Design, build, and operate end-to-end ML infrastructure for training orchestration, experiment tracking, model registries, automated evaluation, and model delivery.
- Own LLM and SLM serving infrastructure, including low-latency, high-throughput inference, batching, caching, and autoscaling.
- Build and manage multi-GPU training and inference clusters across cloud and on-premises environments while optimizing scheduling, utilization, and cost.
- Implement production model observability for latency, throughput, drift, regression, and quality with actionable alerting.
- Apply quantization, distillation support, KV-cache management, and other inference-time deployment optimizations.
- Harden enterprise deployments through reproducibility, versioning, access controls, and audit-ready model traceability.
- Establish MLOps best practices and infrastructure tooling standards as an early senior member of the infrastructure team.
Requirements
- At least five years of experience in MLOps, ML infrastructure, or platform engineering with substantial production ownership.
- Production experience deploying and scaling LLM inference infrastructure using serving frameworks such as TRT, vLLM, SGLang, or TGI.
- Strong proficiency with Kubernetes, Docker, and infrastructure-as-code such as Terraform.
- Hands-on experience managing GPU clusters and distributed training or serving environments.
- Proficiency in Python and experience building substantial, maintainable systems.
- Experience with ML pipeline and orchestration tools such as Airflow, Kubeflow, Ray, MLflow, or Weights & Biases.
- Strong computer science fundamentals and cloud architecture experience with AWS, GCP, or Azure.
- Bachelor’s degree in computer science or a related technical field.
- Preferred: master’s degree, multi-node GPU training experience, quantization and inference optimization experience, Spark familiarity, LLM/VLM fine-tuning workflows, regulated or enterprise experience, or contributions to open-source ML infrastructure projects.
Benefits
- Full-time, on-site role in Palo Alto, California
- Significant equity and premium benefits
- Opportunity to shape infrastructure foundations as an early infrastructure team member
Tech Stack
Categories
About Nace AI
Nace AI builds enterprise AI agents and long-horizon reasoning models that automate financial audit, accounting, and compliance workflows. Its flagship offering includes NAVI for real-time insights across audit, compliance, and operational intelligence, delivered as enterprise software to large organizations. Founded in 2024 and headquartered in Palo Alto, the company is backed by General Catalyst, Walden Catalyst, and Intel.