Machine Learning Engineer, AI Research & Education

Key Skills

Java Programming, Leadership, Design of Experiment, Python Programming, SQL, Big Data Technology, Apache Hadoop, Apache Hive, SPARK Programming, HBase, ETL frameworks

Job Description

Job summary
AWS AI/ML is looking for world class engineers to join its AI Research and Education group working on a new ML-based product at the intersection of of relational learning, semantic representation, natural language processing, and data integration. In this role, you will leverage your expertise in areas such as schema matching, entity recognition and linking, relationship extraction, data lineage identification, text generation, dataset summarization, structured and unstructured data representation learning, and knowledge graphs generation to develop a semantic layer connecting the assets stored in large data lakes.

At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and play a key role in evolving them into large scale production systems. You will interact closely with our customers, engineering, product teams, and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists.

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.

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. 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.

BASIC QUALIFICATIONS

• 4+ years of professional software development experience
• 3+ years of programming experience with at least one software programming language
• 2+ years of experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and current systems
• Experience as a mentor, tech lead OR leading an engineering team
• B.S. degree in computer science, engineering, mathematics, or a related technical discipline
• 3+ years of industry experience in engineering, software engineering, applied science, database administration or a related field with a track record of manipulating, processing, and extracting value from large sets
• Experience using big data technologies (Hadoop, Hive, HBase, Spark etc.)
• Demonstrated strength in data modeling, ETL development, and data warehousing
• Experience building systems to orchestrate batch and streaming pipelines
• Knowledge of data management fundamentals and data storage principles
• Proficiency in SQL and Python or any object oriented programming language

PREFERRED QUALIFICATIONS

• M.S. Degree in Computer Science, Engineering or other related fields
• Experience in building end-to-end ML engineering infrastructure
• Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Experience providing technical leadership and mentoring other engineers for best practices on data engineering