In 2012, Lambda started with a crew of AI engineers publishing research at top machine-learning conferences. We began as an AI company built by AI engineers. That hasn’t changed. Today, we’re on a mission to be the world’s top AI computing platform. We equip engineers with the tools to deploy AI that is fast, secure, affordable, and built to scale. Whether they need powerhouse GPU hardware on-site or the flexibility of cloud-based solutions, we’ve got the horsepower to make it happen. Lambda’s AI Cloud has been adopted by the world’s leading companies and research institutions including Anyscale, Rakuten, The AI Institute, and multiple enterprises with over a trillion dollars of market capitalization. Our goal is to make computation as effortless and ubiquitous as electricity.

If you’d like to build the world’s best deep learning cloud, join us.

• Note: This position requires presence in our Dallas Data Center location 5 days per week.

SALARY RANGE INFORMATION

Based on market data and other factors, the annual salary range for this position is $147,000-$211,000. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

• Manage large scale deployments of GPU clusters in datacenter colocation around the country
• Work closely with Data Center engineering and operations to ensure proper deployment of data center infrastructure requirements
• Drive multiple simultaneous projects forward, assessing risks, and monitoring tasks.
• Proactively manage dependencies and anticipate/resolve execution issues
• Partner with a full spectrum of cross-functional stakeholder, including Product, Engineering and Operations – to help ensure that we are building the right thing, testing correctly, deploying on time and meeting stated SLAs for various programs
• Manage communication of progress/status with internal stakeholders and customer groups in various locations and time zones
• Interact with internal and external stakeholders at various levels concerning resolution of technical and scheduling issues
• Build strong partnerships across Lambda and leverage credibility and technical acumen to drive process improvements and efficiencies
• Contribute to the development of new business opportunities and expansion of existing business opportunities