If you are passionate about building state of the art deep learning algorithms for recommender system and if you like challenging high-performance, low-latency distributed architectures, love learning about complex systems, and, in general, driven by solving problems at scale, Amazon Personalize is looking for you!
Amazon Personalize is a Machine Learning (ML) service that makes it easy for developers to add personalized recommendations for users of their applications. We primarily work on deep learning models for various recommendation products. The models are built using modern deep learning frameworks like MXNet, Keras+Tensorflow and PyTorch. We partner very closely with scientists to develop the network architeture and then we own every single line of model training and inference that we push to production. You will get hands on experience in building a production machine learning model and scaling it to hundreds of customers.
We are one of the fastest growing AWS AI services. We are a collaborative team full of passionate engineers, who collaborate with Applied Scientists and Product Managers on ideas, to rapidly deliver production quality solutions with a broad business impact.
Our entire stack is built using cutting-edge native AWS services and technologies, including AWS Glue, AWS Lambda, ElastiCache, DynamoDB, S3, CloudWatch, ECS, Apache Spark, and ML frameworks like TensorFlow and PyTorch.
Our ideal candidate is passionate about machine learning / deep learning, wants to develop a career as a machine learning engineer, thrives in ambiguity, understands the importance of team collaboration, moves fast, and has a persistent desire to improve how things are done. They will have the technical skills to design and build complex large-scale distributed systems. They prefer working on small, focused teams, have a product-first thinking, and love to pitch in anywhere to get the job done. They lead by example and influence the team with their engineering expertise and decision-making ability. They can communicate effectively (verbal, written, and listening) with a wide variety of audience and are comfortable pitching new ideas.
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. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
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 on machine learning, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. 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.BASIC QUALIFICATIONS
• Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
• Bachelor's degree in computer science related fields OR 1+ years equivalent experience in software developmentPREFERRED QUALIFICATIONS
• Experience working with machine learning systems
• Experience either MXNet, Tensorflow or PyTorch
• Understanding of basic linear algebra
• Experience with recommender systems
• Experience translating ideas and design into concrete requirements.
• Experience programming applications backed by cloud services.
• Experience architecting low-latency Big Data pipelines and Data storage solutions.
• Proficiency in at least two programming languages.
• Interpersonal communication skills, including verbal, written, and listening.
• Experience working in a start-up.
Software and Programming