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AI Engineer (E-Commerce Solutions)

TossSeoul
On-site Full-time

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Experience Level

Experience

Qualifications

# Qualifications- Strong understanding of business requirements with experience in addressing them using ML technologies, including cutting-edge AI techniques (LLM/RAG, LMM).- Proven ability to design models that integrate various data types (text, images, audio, structured data) and adapt quickly.- Familiarity with applying ML technologies in service environments and iteratively improving services, with a holistic view of service functionality.- Experience leading the complete process from problem definition, model design, experimentation, to quantitative evaluation.- Proficient with tools in the latest AI ecosystem such as PyTorch, Hugging Face Transformers, and LangChain.- Experience in designing solutions that consider both technical experimentation and service applicability and scalability.- A passion for exploring and technically defining new problems.

About the job

# About the Team
- The Machine Learning Engineer (Commerce AI) position plays a crucial role in addressing various operational and service challenges within the Toss commerce domain using advanced AI technologies.
- The team tackles complex issues like operation automation, seller evaluation, product information, and search quality that require sophisticated judgment across the commerce spectrum.
- Utilizing a variety of AI technologies such as LLM, RAG, and multimodal models, we design innovative approaches to define and solve problems.
- We go beyond simply solving existing issues; we redefine and expand the problems themselves.
- **Interested in learning more about Toss's data organization?** [→ *Toss Data Division Wiki*](https://recruit-data-division.oopy.io/)

# Responsibilities
- Leverage diverse forms of information including text, images, audio, and structured data to model and resolve complex challenges within the commerce domain.
- Experiment with large language models (LLM), RAG, and multimodal models to explore new solutions for previously unsolvable tasks.
- Investigate opportunities to apply AI to areas that have not been clearly defined before.
- Validate the effectiveness of designed models through offline/online experiments and quantitative evaluations, maintaining iterative improvement routines.
- Design solutions that not only emphasize technical sophistication but also consider business applicability and sustainability.

# Ideal Candidate
- Experience in solving problems using ML technologies, including the latest AI techniques (LLM/RAG, LMM), with a strong understanding of business requirements.
- Proficiency in designing models that integrate various forms of data (text, images, audio, structured data) and rapidly adapting them.
- Familiarity with applying ML technologies to services and improving them iteratively, along with a comprehensive understanding of service architecture.
- Demonstrated leadership throughout the entire process from problem definition to model design, experimentation, and quantitative evaluation.
- Proficiency in the latest AI ecosystem, including tools like PyTorch, Hugging Face Transformers, and LangChain.
- Experience in designing solutions that account for both technical experimentation and service applicability and scalability.
- A keen interest and ability to explore and technically define new problems.

# Resume Tips
- Instead of merely listing modeling techniques, provide concrete examples of improvements made and their impacts.
- Highlight any significant achievements derived from experiments and iterative improvements under various constraints.
- Emphasize your role in collaborative processes and how you contributed to problem-solving beyond just technical aspects.

# Recruitment Process
- Application submission > 1st Technical Interview (Coding) > 2nd Technical Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Acceptance.
- The first interview will include a straightforward coding test, resume review, and basic ML knowledge assessment.
- The second interview will focus on in-depth technical discussions and ML system design.

# A Note for Future Colleagues
> "Solving complex problems with AI is just the beginning.
What we truly focus on is re-examining the problems themselves and transforming them for the better."
- It’s not just about feeding refined data into models; it’s about contemplating how to technically resolve problems that are still undefined.
- We always begin with questions like, "Do we really need to model this?" and engage in a process of redefining the problem to find the most impactful solutions.
- We aim to design new workflows led by AI, rather than merely applying AI to tasks that have been traditionally performed manually.

About Toss

Toss is a pioneering company in the fintech sector, committed to leveraging advanced technologies to enhance commerce and user experiences. Our team thrives on innovation and collaboration, aiming to redefine how services are delivered and experienced in the digital age.

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