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Software Development Engineer – AWS AI SageMaker

Software Development Engineer – AWS AI SageMaker

Job ID 
Company/Location (search) 
Posted Date 
Amazon Corporate LLC
Recruiting Team 

Job Description

Come and be part of the Amazon AI Algorithms team and work on cutting edge machine learning algorithms for the Amazon SageMaker platform!

With Machine Learning, businesses now ask our machines to do more than repetitive, strictly-defined tasks. We are taking it one step further and have begun to ask them to not only learn on their own but to also interpret data and report to the customer before they even knew they needed it. It's a step in history for you to be a part of. You will be building a platform that incorporates best practices and runs advanced algorithms at production scale and reliability.

We are a team of data scientists and engineers who experiment, research, and turn machine/deep learning and AI research into great products for our customers.

You will work in a fast-paced environment and do everything from determining priorities, designing features, re-architecting as necessary, automating testing, and mentoring others. The best candidates show true end-to-end ownership. In this role, you will be responsible for building algorithms, tooling, frameworks, and operational processes using technologies like MxNet, Python, C++, CUDA, Docker, etc.

Basic Qualifications

· Bachelor’s or Ph.D degree in Computer Science or equivalent work experience.

· 5+ years professional experience in software development of multi-threaded, scalable and highly-available distributed systems.

· Computer Science fundamentals in object-oriented design, data structures, high-performance computing.

· Computer Science fundamentals in algorithm design, complexity analysis, problem solving and diagnosis.

· Proficiency in, at least, one modern programming language such as Java, Python, C/C++, C#, Perl.

Preferred Qualifications

· Experience taking projects from scoping requirements through V1 launch and V2 iterations.

· Knowledge of professional software engineering practices and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.

· Experience with highly distributed, multi-tenet systems with clear state-full/state-less boundaries.

· Experience with machine learning, deep learning, data mining, and/or statistical analysis tools.
· Proficiency designing SDKs, frameworks, and working with data science frameworks such as Numpy, MxNet, Tensorflow, etc.
· Passion and experience for mechanical sympathy and performance engineering - in particular using GPGPUs.