Staples is business to business. You're what binds us together.
(Location - Remote)
At Staples our Digital Solutions team is more than a traditional IT organization. We are a team of passionate, collaborative, Agile, inventive, customer-centric, results-oriented problem solvers. We are intellectually curious, love advancements in technology and seek to adapt technologies to drive Staples forward. We anticipate the needs of our customers and business partners, and deliver reliable, customer-centric technology services. If you want to be on the front lines, driving one of the greatest technology transformations of the 21st Century, you should join our team!
Staples' Order Management and Supply Chain Systems team enables scalable, efficient, and intelligent order and delivery solutions, and exceptional customer experience through our expertise in business domains and technologies. We are currently taking a platform re-engineering approach to consolidate, modernize, and simplify legacy applications by standardizing interfaces, building micro-services, and/or integrating with 3rd party software that'll effectively reduce tech debt and vendor footprint, and improve time to market and service levels.
What you'll be doing:
We are looking for dedicated machine learning engineer to help reimagine our systems from the ground up. We need talented people to help design the next generation of technology applications. You are hands-on and will be developing predictive algorithms producing high-level architecture designs, building, and operating services at scale. You will be partnering with a team Software/Data/ML/Applied engineers for building high performing applications that interact with large scale distributed systems.
What you bring to the table:
- Instill best practices in development of algorithms, design, build, and maintain systems that deals with streaming big data for analytics and Machine learning use cases.
- Experience in working in teams in a Dev/ML Ops culture
- Experience with machine learning fundamentals.
- Participate in all phases of software development including concept, design, prototyping, and production release
- Experience with Big Data solutions and knowledge of machine learning fundamentals.
What's needed- Basic Qualifications
- BS in Computer Science, Computer Engineering, Industrial Engineering/OR or related field (MS/PhD preferred)
- 7+ years of professional software engineering experience
- Demonstrated success in development and design of large scale big data systems that perform complex event processing, machine learning and/or data mining
- Expertise in Reinforcement Learning and Combinatorial Optimization
- Strong diagnostic, debugging, and troubleshooting skills
- Experience building applications on one or more public Cloud Platform (e.g. Azure, AWS)
- Experience designing and implementing highly reliable, fault-tolerant distributed ML applications with focus on ML Operations
What's needed- Preferred Qualifications
- Strong scripting skills in a Linux/Unix environment (e.g.: Bash, Python, Perl)
- Experience working with RDBMS and NoSQL data stores on-premise and in the Cloud (e.g. SQL Server/Azure, PostgreSQL/Azure, MongoDB, MySQL, Cosmos DB/Azure, Aurora/AWS, Teradata, Oracle)
- Experience working with data processing frameworks (e.g. Hadoop, Spark, Kafka) a plus
- Prior experience building demand forecasting platforms
- Any Involvment ofhands-on work doing applied research a plus
- Flexible PTO (22 days) and Holiday Schedule (7 observed paid holidays)
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!
Interested in joining the team? Check out our!
Staples believes Inclusion is a verb and we encourage diversity of thinking and ideas as well as backgrounds and experiences. Staples is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other basis protected by federal, state, or local law.