AWS Elastic Compute Cloud (EC2) Capacity Research and Engineering Org is looking for an experienced applied optimization expert. This leader will join the Onhand Packing Science team to design, implement, and scale decision-making algorithms to optimize EC2’s use of existing capacity. The team owns the optimization of all aspects of virtual instances placement across EC2, from high level supply shaping right down the final physical machine a customer’s instance gets allocated to. Given the sheer scale of EC2 any improvement efficiency withithin capacity is utilized has significant business impact.
Key job responsibilities
We are seeking an expert with a strong background in mathematical optimization with excellent modeling skills, with experience using both exact and heuristic methods. The domain requires a tight collaboration with engineering teams, to ensure we are able to quickly move from modelling and prototyping to full scale production usage. You should be confirmable with the needed statistical approaches for production A/B tests as well as have a solid understanding experimental design more broadly. Being successful in the role requires having the scientific breadth to understand the interactions between different phases of a project from data analysis through to production, including resolving issues after rollout.
You will be hands-on with the mathematical modeling and implementation, and will also contribute to the design of the engineering system with the scalability, extensibility, maintainability, and correctness of the optimization engine in mind. You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering / science interface, and computational performance. You will mentor and lead junior scientists by example. Communicating your results to guide the direction of the business and working with software development teams to implement your ideas in code is key to success. You will write technical, and less frequently, business documents that influence engineering investments and business direction. Collaborating with other scientists, software engineers, and product managers, you will develop creative, novel, and data-driven approaches to improve our existing cloud compute offerings and define new ones in a fast-paced and quickly changing environment, improving the experience of our customers and impacting the bottom line of EC2.
- PhD in operations research, applied mathematics, computer science, or similar quantitative field.
- 2+ years of relevant academic research or industry experience in developing algorithms.
- Publications in refereed academic journals or conference.
- In-depth knowledge of exact, approximation algorithms, and heuristic methods for solving difficult optimization problems.
- Ability to productionize models end-to-end.
- Experience in prototyping and developing software in traditional programming languages (Python, C++, Java etc.).
- Comfortable with SQL and experience with very large-scale dataset.
- Excellent writing skills to document the models and analyses and for presenting business cases with results/conclusions in order to influence important decisions.- Experience collaborating with engineers to deliver novel scientific solutions.
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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
