Senior Solutions Architect, Physical AI Cloud

This is a fully remote position, open to applicants in California.

📋 Description

• Assist partners in creating scalable, observable, GPU-accelerated Physical AI pipelines utilizing agentic workflows, cloud-native technologies, and NVIDIA frameworks like OSMO.

• Contribute to the development of Physical AI data factories for tasks including data ingestion, preprocessing, annotation, filtering, synthetic data generation, training, simulation, and evaluation.

• Acquire a comprehensive understanding of robotics workload scaling and convert customer requirements into optimized cloud-native architectures.

• Enhance scheduling, cost, storage access, networking, and GPU utilization across hybrid infrastructures.

• Speed up distributed inference using NVIDIA technologies such as NIM, TensorRT-LLM, vLLM, and SGLang.

• Collaborate effectively with business, engineering, and product teams.

• Offer technical guidance and mentorship to clients implementing Physical AI at scale.

⛳️ Requirements

• Bachelor's degree in Computer Science, Computer Engineering, or a related discipline, or equivalent experience.

• Over 5 years of experience in Solution Architecture or Infrastructure Engineering.

• Proven experience in advancing AI/ML systems from proof of concept to production in private/public cloud settings.

• Experience in scaling Robotics workloads in multimodal model training, inference, robot learning and simulation, or large-scale data processing and generation.

• Strong practical experience in designing, deploying, and managing Kubernetes-based platforms for distributed GPU and AI workloads.

• Proficiency in networking, including DNS, load balancing, TCP/IP, and firewalls.

• Expertise in storage technology.

• Familiarity with workflow orchestration tools such as Airflow and Argo.

• Knowledge of modern DevOps practices including GitOps, Infrastructure as Code (IaC), and observability.

• Experience in orchestrating efficient GPU workloads.

• Exceptional communication skills for conveying technical concepts to varied audiences.

• Preferred: hands-on experience with robotics frameworks like ROS2 and NVIDIA platforms such as Isaac Lab, Isaac Sim, GR00T, or Cosmos.

• Preferred: exposure to large-scale Robotics data curation, annotation, and filtering processes, including AI models for data labeling.

• Preferred: experience with deploying NVIDIA inference technologies such as Dynamo, NIM, Triton, and vLLM utilizing quantization.

• Preferred: proficiency in using and developing agentic workflows.

• Preferred: broad technical knowledge across networking, compute, and storage systems such as S3, NFS, and Lustre, with hands-on experience in building and debugging REST and gRPC APIs.

🏝️ Benefits

• Equity

• Benefits