Software Dev Engineer II, SageMaker Compute Governance
Amazon is hiring a Software Dev Engineer II, SageMaker Compute Governance in Bellevue, United States. It pays $144k-$194k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- Little AI ●○○○ is how Level rates this role: aI is not part of the work.
- Tools and skills the posting cites include SageMaker.
- Level classifies it as a mid-level role.
- Level lists 21,000+ other open roles at Amazon.
AI in this role
We are looking for a Software Development Engineer to help build the systems that put idle or inefficiently used GPUs to work. You will design and build distributed backend services that let organizations pool capacity across many clusters and accounts, route it to the workloads that need it most, and see exactly how it is being used. This is deep systems work at the intersection of large-scale distributed systems, container orchestration, and cloud infrastructure, and it directly changes how much value customers get from every GPU they pay for.
You will own services end to end, from design through launch and operation in production, and your work will ship to some of the largest AI compute customers in the world.
Key job responsibilities
Design, build, and operate distributed backend services that manage and share accelerated compute across multiple clusters and accounts.
Build scheduling and capacity-sharing capabilities that move compute to the highest-priority workloads and reclaim it when priorities change.
Build zero-touch usage and guidance systems that collect, aggregate, and report how compute is consumed, at scale and in near real time.
Own your services in production: define operational metrics, respond to issues, and continuously improve reliability, latency, and cost.
Write clear design documents, participate in design and code reviews, and raise the engineering bar for the team.
Collaborate with product, design, and partner engineering teams to turn customer problems into shipped features.
Mentor other engineers and contribute to a culture of ownership, quality, and customer focus.
A day in the life
Most days start with a short team stand-up, then it is mostly building. You might design a service that lets one cluster borrow idle capacity from another, then write it up for a design review where the team pokes holes in it before any code is written. Hard problems get their own deep-dive whiteboard session. Other days lean toward operations, chasing down why a workload did not get the capacity it should have. You will not be handed fully specified tasks; you will have the independence to help decide what to build, then build it.
About the team
We help customers get the most out of the accelerated compute they pay for. Our mission is simple to say and hard to do: make sure every accelerator a customer pays for is doing useful work, and make that easy to see and control. Cost reduction is one important part of that, but the bigger idea is the engine that delivers compute to the workloads that need it most, in as little time as possible.
Our customers run some of the largest AI training and inference workloads in the world, and they feel the impact of our work directly in how much they can do with their infrastructure. We care about depth, we own our work end to end, and we build in the open where we can.
Basic qualifications
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language
Preferred qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, BELLEVUE - 143,700.00 - 194,400.00 USD annually
How we rate this
Software Dev Engineer II, SageMaker Compute Governance at Amazon rates 9 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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