Posted:
11/25/2024, 8:02:41 PM
Location(s):
Indiana, United States ⋅ Bengaluru, Karnataka, India ⋅ Indianapolis, Indiana, United States ⋅ Karnataka, India
Experience Level(s):
Mid Level ⋅ Senior
Field(s):
AI & Machine Learning ⋅ Data & Analytics
Workplace Type:
Hybrid
About Team
The Catalog Data Science Team at Walmart Global Tech is focused on using the latest research in generative AI (GenAI), artificial intelligence (AI), machine learning (ML), statistics, deep learning, computer vision and optimization to implement solutions that ensure Walmart’s product catalog is accurate, complete, and optimized for customer experience. Our team tackles complex data science and ML engineering challenges related to product classification, attribute extraction, trust & safety, and catalog optimization, empowering next-generation retail use cases.
The Data Science and ML Engineering community at Walmart Global Tech is active in most of the Hack events, utilizing the petabytes of data at our disposal, to build some of the coolest ideas. All the work we do at Walmart Global Tech will eventually benefit our operations & our associates, helping Customers Save Money to Live Better.
What you'll do:
We are looking for a Staff Machine Learning Engineer who can help build large scale AI/ML/Optimization products. Expected qualities include ability to build, deploy, maintain and troubleshoot large scale systems.
As a Staff ML Engineer, you’ll have the opportunity to
Lead and inspire a team of scientists and engineers solving AI/ML problems through R&D while pushing the state-of-the-art
Lead the team to develop production level code for implementation of AI/ML solutions using best practices to handle high scale and low latency requirements
Design large scale AI/ML products/systems impacting millions of customers
Develop highly scalable, timely, highly-performant, instrumented and accurate data pipelines
Identify, develop, and deliver improvements on data performance, data quality and cost, which needs to be monitored and analyzed
Enable data governance practices and process by being a passionate adopter and ambassador
Perform fine-tuning operations on large language models to customize them for specific business needs
Optimize large language models for performance and scalability in production environments
Utilize and contribute to generative AI advancements to develop novel solutions that meet business requirements
Collaborate with multiple stakeholders to drive innovation at scale
Build a strong external presence through publishing your team’s work in top-tier AI/ML conferences and developing partnerships with academic institutions
Adhere to Walmart’s policies, procedures, mission, values, standards of ethics and integrity
Adopt to Walmart’s quality standards, develop/recommend process standards and best practices across the retail industry
What you'll bring:
PhD with >5 years of relevant experience / 4-year bachelor’s degree with > 10 years of experience / Master’s degree with > 8 years of experience. Educational qualifications should be preferably in Computer Science or a strongly quantitative discipline.
Demonstrated history of technology leadership and strong hands-on experience
Proven records of scientific publications or intellectual property generation
Past experience in strong programming skills across big data and ML engineering stack
Strong communication skills with inclination to high ownership and commitment
Prior experience leading AI/ML tracks and taking it to Production
About Global Tech
Imagine working in an environment where one line of code can make life easier for hundreds of millions of people and put a smile on their face. That’s what we do at Walmart Global Tech. We’re a team of 15,000+ software engineers, data scientists and service professionals within Walmart, the world’s largest retailer, delivering innovations that improve how our customers shop and empower our 2.2 million associates. To others, innovation looks like an app, service or some code, but Walmart has always been about people. People are why we innovate, and people power our innovations. Being human-led is our true disruption.
Flexible, hybrid work:
We use a hybrid way of working with primary in office presence coupled with an optimal mix of virtual presence. We use our campuses to collaborate and be together in person, as business needs require and for development and networking opportunities. This approach helps us make quicker decisions, remove location barriers across our global team, be more flexible in our personal lives.
Benefits:
Beyond our great compensation package, you can receive incentive awards for your performance. Other great perks include a host of best-in-class benefits maternity and parental leave, PTO, health benefits, and much more.
Equal Opportunity Employer:
Walmart, Inc. is an Equal Opportunity Employer – By Choice. We believe we are best equipped to help our associates, customers, and the communities we serve live better when we really know them. That means understanding, respecting and valuing diversity- unique styles, experiences, identities, ideas and opinions – while being inclusive of all people.
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Minimum Qualifications:Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field. Option 3: 6 years' experience in an analytics or related field.Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.
Website: http://www.walmart.com/
Headquarter Location: Bentonville, Arkansas, United States
Employee Count: 10001+
Year Founded: 1962
IPO Status: Public
Last Funding Type: Post-IPO Debt
Industries: E-Commerce ⋅ Grocery ⋅ Retail ⋅ Retail Technology ⋅ Shopping