ADCI
E-Commerce
AppliedScientist,TrustworthyShoppingExperience(TSE)
“Applied Scientist, Trustworthy Shopping Experience (TSE) at ADCI. Skills: Generative AI, Agentic AI, Machine Learning, Deep Learning. Contribute to design and development of agentic AI. Reasoning”
What You'll Achieve.
Guarantee customers worry-free shopping experience; Earn customer trust; Reduce cost-of-serving; Maintain trust and safety standards; Serve hundreds of millions of customers
Industry & Context.
Solve complex business problems; Map business goals to scientific problems; Troubleshoot issues to root cause; Identify customer and business problems; Escalate problems with proposed solutions
What They're Looking For.
Must Have
Experience programming in Java, C++, Python or related language, Experience with SQL and an RDBMS or Data Warehouse, Experience building machine learning models or developing algorithms, Experience in deep learning models architecture design, Experience in deep learning training and optimization, Experience in model pruning
Nice to Have
Experience implementing algorithms using toolkits, Experience implementing algorithms using self-developed code, Publications at top-tier peer-reviewed conferences or journals, Publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS
What You'll Do.
Contribute to design and development of agentic AI
Autonomous task execution
Multimodal intelligence
Leverage reinforcement learning techniques
Agent decision-making
Design and build expertise agentic AI systems
Long term memory mechanisms
Short term memory mechanisms
Productionize large scale models
Supervised Finetuning
Reinforced fine tuning
Process/Outcome Reward Models
Contrastive approaches
Prototypical approaches
Enhance Automatic prompt optimization techniques
Ground truth datasets
Improve agentic planning
Build novel production ready Finetuned transformer architectures
Conventional supervised ML solutions
Unsupervised ML solutions
Aid automation requirements
Identify customer and business problems
Extend state-of-the-art approaches
Complex LLM workflows
Contribute to building production-ready deep learning solutions
Conventional ML solutions
Cross-modal alignment techniques
Connect visual understanding
Textual understanding
Relational understanding
Support automation requirements
Co-author research papers
Peer-reviewed internal venues
Peer-reviewed external venues
Multimodal representation learning
Vision-language modeling
Review research submissions
Iterate based on feedback
Deliver small components
Multimodal data pipelines
How You'll Work.
Team & Collaboration
Working alongside senior scientists; Participate in team design discussions; Participate in prioritization discussions; Participate in engineering best practices; Peer review code; Mentor interns
Communication Scope
Clear verbal communication; Clear written communication; Publish internal technical reports; Communicate design
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