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.
About the Role
As an AI Solutions Engineer, you will partner with customers working on deep learning at large enterprises, major research institutions, and venture-backed startups, to design compute infrastructure that meets their specific needs. While some customers require only a single workstation or server, others need large compute clusters and you will provide guidance that is tailored both to the customer’s deep learning application and to the scale at which they are currently operating.
What You’ll Do
- Advocate for Lambda’s Products.
- Develop and maintain expertise in Lambda’s hardware and software tools.
- Demonstrate Lambda’s hardware and software to customers, partner companies, and staff.
- Write internal and public technical content.
- Provide technical feedback from customers to Lambda’s product and marketing teams.
- Own the technical side of Lambda’s sales process.
- Partner with Lambda account managers to provide an excellent customer experience throughout the sales process.
- Work with deep learning engineers and IT professionals from our customer’s internal teams to identify the challenges and bottlenecks they hope to overcome.
- Design compute solutions that meet the customer’s needs by applying your technical skills, seeking customer feedback, and iterating as needed.
- Document your designs in various formats including white-papers, wire diagrams, BoMs, and rack elevations.
- Develop and maintain expertise in compute for deep learning.
- Build structured and purposeful learning into your work routine.
- Become an expert in GPU training and inference workloads.
- Stay up to date on the latest deep learning compute hardware trends and experiment with them using internal tools and resources.
- Develop high quality processes and documentation.
- Reinforce Lambda’s positive culture throughout the organization.
- Love learning both broadly and deeply.
- Are a skilled communicator who can translate technical concepts into plain english and vague customer needs into technical requirements on the fly.
- Are able to craft excellent technical documentation.
- Have had a technical role working with computer hardware on a pre-sales or engineering team.
- Are comfortable executing simple commands in Ubuntu or another Linux distribution.
- Measure yourself on results, not effort, and constantly seek to accomplish more by becoming more efficient.
- Are able to build strong relationships across your entire organization.
- Listen carefully and understand deeply.
Nice To Have
- Experience with GPU distributed training
- Experience with MLOps platforms
- Experience with a parallel file system
- Experience with data center networking equipment
- Experience with SLURM, Kubernetes, or other job scheduling systems
- We offer generous cash & equity compensation. Cash Compensation Range: $111,600 – $147,600
- 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 100, and growing fast
- Our remote workforce, based on role, is across the U.S., with headquarters in San Francisco
- 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.
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.