Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.
The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).
As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.
The primary responsibilities of this role are to:
- Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries
- Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them
- Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithms
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.
This position involves on-call responsibilities, typically for one week every two months. We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don't get paged for the same issue twice.
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. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. 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.
- Graduate degree (MS or PhD) in computer science, engineering, mathematics or related technical/scientific field
- 5+ years of professional experience in a business environment
- 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems
- 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM)
- Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, Keras, Caffe, PyTorch, or similar
- Experience in using Python or other programming languages
- PhD degree in computer science, engineering, mathematics, or related technical/scientific field
- Experience with machine learning, time series, NLP and CV solutions
- Strong communication and presentation skills
- Strong attention to detail
- Comfortable working in a fast paced, highly collaborative, dynamic work environment
- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.