AI Jobs in Seoul

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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The Forward Deployed Engineer Lead is responsible for partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. They architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. The role involves collaborating with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation. They own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, they shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer on Reflection's Applied AI team, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments, including public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. You will also contribute to evolving playbooks, processes, and best practices as part of a growing Forward Deployed Engineering organization.

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As a Forward Deployed Engineer Lead, you will own the end-to-end technical strategy, execution, and delivery of complex agentic applications, from early pre-sales discovery through production deployment. Responsibilities include partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. You will architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. Collaboration with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation is required. You will own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, you will shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer at Reflection, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments such as public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. Additionally, you will contribute to evolving playbooks, processes, and best practices as part of the growing Forward Deployed Engineering organization.

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You will explore and build experimental AI experiences that help define how people interact with proactive AI systems, working at the intersection of design, engineering, and AI experimentation. Responsibilities include prototyping experimental AI interfaces beyond standard chat UI, building quick proof-of-concept experiences using AI models, APIs, and frontend tools, exploring new ways for users to understand, direct, and collaborate with AI systems, testing interaction ideas around human control, trust, transparency, memory, multimodal input, and long-running workflows, working with product, design, and ML teams to turn early ideas into testable prototypes quickly, identifying useful HCI patterns from research, products, and emerging tools to test their applicability for A1, documenting experiments clearly by detailing what was tested, what worked, what failed, and suggesting what should be built next, and contributing to the team's understanding of what HCI should look and feel like in a real consumer app.

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Work directly with engineers on system design, evaluation, and trade-offs while defining requirements and shaping how the AI system works for global users. Research and define end-to-end AI system requirements from capability to behavior to user impact. Translate model capabilities, data constraints, and evaluation results into clear product and system decisions. Make trade-offs across quality, latency, cost, reliability, and user experience. Collaborate closely with ML, backend, and mobile engineers on system design, evaluation, and iteration. Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback. Drive execution with clear specifications, strong judgment, and disciplined prioritization. Ensure systems ship quickly, safely, and reliably with strong feedback loops. Own product quality end-to-end, ensuring correctness, predictability, and user trust.

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Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems managing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership. Own production deployment including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems into backend, mobile, and desktop products. Make pragmatic trade-offs, ship improvements quickly, and learn from real usage. Work under real production constraints including latency, cost, reliability, and safety. Detect, debug, and resolve production issues quickly to minimize user impact. Support and align team members to deliver high-impact ML work with minimal friction. Ensure iterations on models and systems are measurable, safe, and improve user experience over time.