HPC Systems Engineer

Lambda's GPU cloud is used by deep learning engineers at Stanford, Berkeley, and MIT. Lambda's on-prem systems power research and engineering at Intel, Microsoft, Kaiser Permanente, major universities, and the Department of Defense.

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

*Note: This position requires regular presence in our San Jose Headquarters and adjacent data center

What You’ll Do

• Contribute to designing multi-million-dollar deep-learning clusters for the Fortune 500, Federal Government, and major research institutions
• Execute the full product development lifecycle for Lambda’s clusters, including:
• Requirements gathering and design conceptualization
• Validating requirements with industry research, suppliers, and finance
• Assembling test environments that include compute, storage, networking, and software to validate and document the design, including the installation and configuration of software packages
• Building documentation packages for manufacturing, delivery, and support, and working with those organizations to refine the design for improved supportability by those teams
• Serve as a resource to our sales and sales engineering teams to ensure the customer requirements can be addressed by our solutions
• Engage across the organization during all phases of the product lifecycle 

You

• Have experience in systems design and familiarity with high-performance computing systems
• Have experience installing, integrating, and configuring servers, storage, and networking in data centers and/or labs
• Work well in high-paced environments and with high-performance teams
• Have an innate attention to detail
• Can navigate Linux as a system admin with competency
• Enjoy learning and sharing knowledge

Nice to have

• 2-5 years of experience in engineering or sales engineering for enterprise hardware and/or software.
• Direct experience with HPC clusters for internal or external customers 
• Experience with HPC/AI technologies such as: 
• SFP+ fiber, InfiniBand (IB) / 100 GbE network fabric experience  
• Ethernet, switching, power infrastructure, GPU direct, RDMA, NCCL, Horovod environment
• SLURM, Kubernetes, or other job scheduling systems
• Distributed GPU training systems and third-party ML Ops platforms
• Understanding of the hardware, software, and tools used for deep learning
• Experience working with the technologies that underpin our business such as: Deep Learning frameworks, GPU acceleration, virtualization, and cloud computing.
• Self-directed, results driven mindset
• Bachelor's degree in IS, EE, CS, Physics, Mathematics, or equivalent work experience

About Lambda

• We offer generous cash & equity compensation
• Investors include Gradient Ventures, Google’s AI-focused venture fund
• We are experiencing extremely high demand for our systems, with quarter over quarter, year over year profitability
• Our research papers have been accepted into top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
• We have a wildly talented team of 130, and growing fast
• Our remote workforce, based on role, is across the U.S., with headquarters in San Jose, CA
• Health, dental, and vision coverage for you and your dependents
• Commuter/Work from home stipends
• 401k Plan
• 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.

Salary Range Information 

Based on market data and other factors, the salary range for this position is $120,000-$180,000. 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.

Equal Opportunity Employer

Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.