Machine Learning Engineer II, Professional Services, Insights Team

Key Skills

Java Programming, Python Programming, SQL, AWS, Machine learning techniques, FPGAs, NumPy, Pandas, PyTorch, SciPy, TensorFlow, Apache Airflow, Continuous Integration & Continuous Delivery - CI/CD, Scikit-learn, Amazon RedShift, Amazon EMR, Kubeflow, MxNet, Lambda, SparkML, Batch Data Processing, PyCaret

Job Description

At Amazon Web Services (AWS), we’re hiring technical Machine Learning Developers to collaborate with our Data Scientists to deliver ground-breaking solutions for customers. We are looking for Engineers with Data Science experience and Data Scientists with Engineering experience. We want to take your full-stack Data Science experience to a new level by empowering AWS customers to maximize the benefits they receive through AI/ML on the AWS platform. This means building and operationalizing ML and DL solutions for our customers while helping them adopt modern Machine Learning best practices throughout every stage of their model development lifecycle.

We pride ourselves on being customer obsessed and highly focused on the ML enablement of our customers. If you have experience with ML, including building, deploying, and monitoring models, we’d like you to join our team. A familiarity with cloud solutions (not necessarily AWS) and DevOps best practices is key as you will work with teams of Data Scientists, Data Engineers, and Architects to build truly end-to-end solutions. Without exception, you MUST be prepared and eager to learn new technologies in this role.

Key job responsibilities

You will provide deep and broad insight to customers and partners to help remove constraints that prevent them from leveraging AWS services to create strategic value. A commitment to team work, hustle, and communication skills are important in this role. Creating reliable, scalable, and high-performance AI/ML solutions requires strong technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience building large-scale distributed systems.

About the team

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 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

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 professional and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• Bachelors degree in computer science, engineering, data science, or related technical, math, or scientific field
• 3+ years of industry experience developing applications, designing data architectures (e.g. data pipelines, distributed computing engines, ML infrastructure design), or writing software using scripting languages (e.g. Python, R), database languages (e.g. SQL, PL/SQL, PG-PL/SQL), and version control
• 3+ years experience performing Data Scientist duties (e.g. ML algorithm selection, feature engineering, model training, hyperparameter tuning, distributed model training, supervised and unsupervised learning implementation, building model pipelines, using Machine Learning tools/libraries/frameworks)
• 3+ years MLOps experience (e.g. model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning)
• Experience using data science tools, libraries, and frameworks (e.g. Scikit-learn, caret, mlr, mllib, SparkML, NumPy, SciPy, Pandas, TensorFlow, PyTorch, MXNet)
• Experience with Apache Spark and Amazon AWS platform (SageMaker, Redshift, EMR, Glue, Step Functions, Lambda, Batch, etc.)

PREFERRED QUALIFICATIONS

• Masters degree in computer science, engineering, data science, or related technical, math, or scientific field
• Experience creating orchestration workflows with tools such as Airflow, Kubeflow, or AWS Step Functions
• DevOps experience (e.g. CI/CD Pipelines, Infrastructure as Code, containers, Agile software development)
• Experience writing production-level code using object-oriented design (OOD) best practices
• 5+ years of Machine Learning Experience or a Software Engineer or a Data Engineer
• 5+ years MLOps experience (e.g. model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning)
• DevOps experience (e.g. CI/CD Pipelines, Infrastructure as Code, containers, Agile software development)
• Experience with end-to-end software development and life cycle of machine learning solutions

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.