Machine learning engineer II, AWS Inference Optimization

Amazon is hiring a Machine learning engineer II, AWS Inference Optimization in Santa Clara, United States. It pays $165k-$224k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
The posting mentions Bedrock, vLLM and SageMaker among its tools and skills.
Level classes it as a mid-level role.
Amazon has 21,000+ other open roles on Level.
Posted in the last few days.

AI in this role

bedrockvllmsagemaker

Amazon AI is looking for a machine learning engineer to be part of inference optimization team working on optimizing large language OSS models like Qwen, Gemma, gptoss, improving inference performance by development on inference stacks like vLLM, SGLAng, performance tuning of GenAI inference on Amazon Sagemaker, Bedrock and Hyperpod. Work includes implementation of inference optimization techniques like Speculative decoding, Disaggregated Inference and CUDA kernel tuning.

As part of the team, you will own price-performance of amazon LLM inference on Sagemaker, Hyperpod and Bedrock. You will be working in a fast-paced, cross-disciplinary team of engineers and researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will deliver these to production in customer facing products.

Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.

Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

Key job responsibilities
As a MLE in Inference Optimization team you will be responsible for the major portion of existing and/or new inference software. List of responsibilities includes but is not limited to developing model servers, developing CUDA kernels in vLLM, SGLang, developing services for GenAI optimization and benchmarking, creating and modifying a mid-size application, or service.

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
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

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.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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, CA, Santa Clara - 165,200.00 - 223,600.00 USD annually

How we rate this

Machine learning engineer II, AWS Inference Optimization at Amazon rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● 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.

Key facts

Machine learning engineer II, AWS Inference Optimization is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $165k-$224k a year.
The role is based in US, CA, Santa Clara.
The job was posted on October 24, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

BedrockvLLMSageMaker

Also common in machine learning engineer jobs at this level

Python PyTorch TensorFlow Deep learning Kubernetes Spark

Pay

This job starts at: $165k
Typical machine learning engineer job: $199k

This posting starts 17% below the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

What's a project where you used Bedrock hands-on?
Walk me through how you've used vLLM in your day-to-day work.
What are the limits of SageMaker that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Bedrock, vLLM, and SageMaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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