Description Are you excited about Machine Learning, chip acceleration, compilers, storage, systems or EC2? Are you passionate about delivering high quality services that affect hundreds of thousands of users? We are the dubbed the "secret sauce" behind AWS's success with development centers in the U.S. and Israel, Annarpuna is at the forefront of innovation by combining cloud scale with the world’s most talented engineers. The Annapurna team hires for multiple disciplines Software and Hardware engineers including but not limited to complier engineer, machine learning engineer, runtime engineer, performance engineer and ML chip accelerator, ASIC, physical designs, SDE in Test. Because of our teams’ breadth of talent, we’ve been able to improve AWS cloud infrastructure in networking and security with products such as AWS Nitro, Enhanced Network Adapter (ENA), and Elastic Fabric Adapter (EFA), in compute with AWS Graviton and F1 EC2 Instances, in machine learning with AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe. As an SDE ML Apps Engineer, you will work alongside Research Engineers and Applied Scientists to build backend science components, including deep learning models that power our platform. Our platform enables non-tech-savvy customers to understand and solve their computer vision problems. Key job responsibilities - Innovating and delivering creative SW Designs to develop new services, solve operational problems, drive improvements in developer velocity, or positively impact operational safety - Writing requirements capturing documents, design documents, integration test plans, and deployment plans - Communicating status and progress of deliverables to schedule, and sharing learnings/ innovations with your team and stakeholders Basic Qualifications - Currently enrolled in, or completed a Bachelor’s degree program or higher in Computer Science, Computer Engineering, Electrical Engineering or related field - To qualify, applicants should have earned a Bachelor’s or Master’s degree between April 2022 to September 2024. Possible start dates for this role are between March 2024 to October 2024. - Programming experience in internship or coursework with programming language such as Python and/or C or C++. - 1+ years of internship or coursework in deep learning, transformer architectures Preferred Qualifications • Previous software engineer (internship/professional) experience with Pytorch/Jax/Tensorflow, Distributed libraries and Frameworks, end-to-end model training, and sharding. The group presents lot of opportunity for optimization and scaling large deep learning models on Trainium (AWS Machine Learning acceleration) architecture. • Experience with distributed, multi-tiered systems, algorithms, and relational databases. • Experience in optimization mathematics such as linear programming and nonlinear optimization Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $99,500/year in our lowest geographic market up to $200,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.