Senior ML Research Engineer, Multimodal Structure & Marengo

3 days ago

Seoul, Korea, SouthSenior

Responsibilities

Set technical direction for multimodal structure and define how video, audio, text, and document assets are organized into reusable, addressable units.
Define architectures, training strategies, and data strategies for next-generation multimodal embedding models.
Own end-to-end model development from research planning through distributed training, production evaluation, and iteration.
Architect and optimize distributed training pipelines, data processing systems, experiment workflows, and GPU utilization.
Own production model APIs, including model packaging, API design, inference optimization, and reliable operation at scale.
Build large-scale data curation, filtering, and quality systems for structure and embeddings.
Define evaluation methods and quality standards for structure, embeddings, multimodal understanding, and retrieval.
Drive the interface between multimodal structure and semantics and align model integration with Agent, Search, Product, and Infrastructure teams.
Provide technical leadership through design and experiment reviews, architectural decisions, and mentorship.

7+ years of industry experience in computer vision, video understanding, or multimodal learning.
Demonstrated ability to take ambiguous research problems from identification through delivery of impactful solutions.
Deep expertise in large-scale distributed model training, including kernel optimization, FSDP, or similar techniques.
Experience building and operating production model APIs for large-scale ML systems.
Deep expertise in video understanding, multimodal representation learning, or foundation model development.
Experience connecting multimodal structure with embeddings and retrieval in end-to-end systems.
Strong proficiency in Python and PyTorch.
Evidence of both research depth and engineering impact, including publications paired with shipped products.
Experience training models at billion-parameter scale.
Experience with training operations, large-scale data curation and quality systems, temporal/spatial/hierarchical modeling, and multimodal video modeling.
Deep experience optimizing training and inference systems for throughput, latency, GPU efficiency, and scale.
Track record of technical leadership and architectural decisions shaping team or product direction.

Hybrid work arrangement with autonomy and collaboration.
Latest MacBook, home-office equipment support valued at 700,000 KRW, and equipment replacement every three years.
Unlimited LLM tokens for technology employees using AI responsibly and efficiently.
Annual professional-development support valued at 1.4 million KRW for courses, conferences, and memberships.
English education and global buddy programs.
Taxi fare support for late-night and weekend commuting.
Annual corporate card allowance valued at 7.2 million KRW for meals, transportation, and other permitted expenses.
Office snack bar and dinner support after 7 p.m. when working in the office.
Annual health checkups for the employee and one family member, group insurance, and flu-shot support.
Two-week paid holiday break at year end.
Three-month probation period with 100% salary paid during probation.

51-200 employees