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.
