Job Description
This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.
Company Overview
A technology company specializing in Embodied AI and advanced artificial intelligence systems for automated driving. Its AI software and foundation models are designed to enable vehicles to perceive, understand, and navigate complex environments without relying on traditional rule-based approaches. The company develops intelligent, mapless, and hardware-agnostic technology while building large-scale AI infrastructure to support model training, evaluation, and deployment.
Responsibilities
- Define and evolve the architecture for allocating and orchestrating AI training and inference workloads across thousands of GPUs and multiple data centers.
- Design compute infrastructure that delivers high throughput, resilience, scalability, and cost efficiency across large-scale AI workloads.
- Architect petabyte-scale data federation systems that provide fast and reliable access to high-volume sensor and simulation data across geographic regions.
- Develop infrastructure capable of supporting the organization’s progression toward exabyte-scale data requirements.
- Build the foundations required to execute large-scale GPU workloads across hybrid and multi-cloud environments.
- Advise senior leadership on cloud infrastructure investments and architectural decisions aligned with company strategy, growth plans, and performance objectives.
- Provide technical leadership through architectural guidance, technical deep dives, and mentorship across the engineering organization.
- Promote operational excellence and strong engineering practices across teams.
- Guide the evolution of foundational compute and storage systems supporting the AI model development lifecycle.
- Help accelerate AI research and model deployment by ensuring infrastructure can meet demanding training and inference requirements.
Qualifications
- 10+ years of experience designing and building large-scale distributed systems.
- At least 4 years of experience focused on GPU-based cloud infrastructure.
- Proven experience supporting large-scale AI training, inference, or computer vision workloads within GPU clusters.
- Deep understanding of petabyte-scale data architecture, including storage federation, high-throughput data access, and data locality for AI workloads.
- Strong technical leadership experience, including defining and communicating architectural strategies while balancing long-term objectives with delivery requirements.
- Demonstrated ability to mentor engineers and influence technical direction across multiple teams.
- Advanced degree in Computer Science, Electrical Engineering, or a related discipline, or equivalent industry experience.
- Experience with multi-cloud orchestration, particularly for latency- or cost-sensitive training and inference pipelines, is desirable.
- Familiarity with technologies such as Ray, Kubernetes, Airflow, or Flyte is desirable.
- Strong understanding of AI/ML job scheduling, model lifecycle management, and infrastructure-as-code practices.
- Experience supporting safety-critical or real-time inference applications, such as robotics, autonomous vehicles, or aerospace, is desirable.
- Interest in building infrastructure-as-a-product that provides strong performance, scalability, and simplicity for research and product teams.
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals globally.
- No Endorsement: SpringCube does not endorse or recommend any company or position listed.
- No Client Relationship: Listing a position does not mean SpringCube has a client, partnership, or employment relationship with the hiring company.
- To Apply: Use the Apply button to apply directly through the relevant hiring company’s application process.
- No Liability: SpringCube is not responsible for the hiring process, employment terms, recruitment decisions, or outcomes associated with any listed position.
