Remotely deploy and configure large-scale HPC clusters for AI workloads (up to many thousands of nodes)
Remotely install and configure operating systems, firmware, software, and networking on HPC clusters both manually and using automation tools
Troubleshoot and resolve HPC cluster issues working closely with physical deployment teams on-site
Provide clear and detailed requirements back to other engineering teams on gaps and improvement areas, specifically in the areas of simplification, stability, and operational efficiency
Contribute to the creation of and maintenance of Standard Operating Procedures
Provide regular and well-communicated updates to project leads throughout each deployment
Mentor and assist less experienced team members
Stay up-to-date on the latest HPC/AI technologies and best practices
You
Are a deeply experienced HPC engineer comfortable with logical provisioning of a cluster
Have a strong understanding of HPC/AI architecture, operating systems, firmware, software, and networking
5+ years of experience in deploying and configuring HPC clusters for AI workloads
Have an innate attention to detail
Are in expert in configuring and troubleshooting:
SFP+ fiber, Infiniband (IB), and 100 GbE network fabrics
Ethernet, switching, power infrastructure, GPU direct, RDMA, NCCL, Horovod environments
Linux based compute nodes, firmware updates, driver installation
SLURM, Kubernetes, or other job scheduling systems
Work well under deadlines and structured project plans also knowing when and how to ask for changes to project timelines
Have excellent problem solving and troubleshooting skills
Have flexibility to travel to our North American data centers as on-site needs arise or as part of training exercises
Are able to work independently and as part of a team
Are comfortable mentoring and supporting junior HPC engineers on cluster deployments
Requirements
Experience with machine learning and deep learning frameworks (PyTorch, Tensorflow) and benchmarking tools (DeepSpeed, MLPerf)
Experience with containerization technologies ( Docker, Kubernetes)
Experience working with the technologies that underpin our cloud business ( GPU acceleration, virtualization, and cloud computing)
Keen situational awareness in customer situations, employing diplomacy and tact
Bachelors degree in EE, CS, Physics, Mathematics, or equivalent work experience
Salary Range Information
The annual salary range for this position has been set based on market data and other factors. 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.
About Lambda
Founded in 2012, with 500+ employees, and growing fast
We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
Our values are publicly available: https://lambda.ai/careers
We offer generous cash & equity compensation
Health, dental, and vision coverage for you and your dependents
Wellness and commuter stipends for select roles
401k Plan with 2% company match (USA employees)
Flexible paid time off plan that we all actually use
A Final Note:
You do not need to match all of the listed expectations to apply for this position. We are committed to building a team with a variety of backgrounds, experiences, and skills.
Equal Opportunity Employer
Benefits
Health insuranceDental insuranceVision insurance401(k)Remote work optionsFlexible scheduleEquity / stock options
Additional Information
Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.
If you'd like to build the world's best AI cloud, join us.
*Note: This position requires presence in our San Francisco / Bellevue office location 4 days per week; Lambda's designated work from home day is currently Tuesday.
Engineering at Lambda is responsible for building and scaling our cloud offering. Our scope includes the Lambda website, cloud APIs and systems as well as internal tooling for system deployment, management and maintenance.
Lambda · San Francisco Office (fremont St)