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Software Engineer Korea Jobs (NOW HIRING)

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Software Engineer, Ads Delivery

San Jose, CA · On-site

$162K - $316K/yr

From 2019 to 2023, Pangle has successively launched in Japan, South Korea, Southeast Asia, the ... entire software development life cycle, including product discussion, requirement analysis ...

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Software Engineer Korea information

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$63.5K

$147.5K

$205.5K

How much do software engineer korea jobs pay per year?

As of Aug 18, 2026, the average yearly pay for software engineer korea in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What is a software engineer Korea?

A Software Engineer in Korea is responsible for designing, developing, and maintaining software applications. They work with programming languages like Java, Python, or JavaScript and collaborate with teams to build efficient software solutions. Engineers in Korea often work in industries such as technology, finance, gaming, and e-commerce. Depending on the company, they may also need to understand Korean business culture and language.

What are the key skills and qualifications needed to thrive in the software engineer Korea position, and why are they important?

To thrive as a Software Engineer in Korea, you need strong skills in programming languages such as Java, Python, or C++, a solid understanding of algorithms, and a relevant degree in computer science or a related field. Familiarity with common development tools (e.g., Git, Docker, CI/CD pipelines), cloud platforms, and certifications like AWS Certified Developer or Google Associate Cloud Engineer are advantageous. Adaptability, effective communication—especially in multicultural teams—problem-solving, and a collaborative mindset are valuable soft skills. These competencies are crucial for building robust software, working efficiently in dynamic team settings, and meeting the expectations of Korea's technologically advanced market.

What is the typical work environment and team structure for software engineers in Korea?

Software Engineers in Korea often work in agile, collaborative environments with multidisciplinary teams that may include developers, designers, product managers, and QA engineers. Team structures can range from small startup teams with flexible roles to larger, more specialized departments within multinational corporations. Most companies emphasize regular communication, participation in daily stand-ups, and use of project management tools such as Jira or Trello to track progress. The work culture in Korea tends to value both technical excellence and collective responsibility for project outcomes, providing opportunities for mentorship and continuous learning.

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Software Engineer Korea jobs near you
Infographic showing various Software Engineer Korea job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

Senior AI Engineer - Machine Learning (US)

Gauss Labs

Palo Alto, CA • On-site

$144K - $189K/yr

Full-time

Re-posted 5 days ago

Job description

Gauss Labs builds Industrial AI for the world's leading manufacturers, applying state-of-the-art ML to large volumes of real production data. A core focus of this role is building our internal tabular foundation models - pretraining, fine-tuning, and serving them in large-scale production systems. As a Senior/Staff AI Engineer, you will turn ML research into robust, scalable production systems and own them across their full lifecycle. You'll work with AI Scientists, Software Engineers, and Program Managers across Palo Alto, CA, and Seoul, South Korea.
Responsibilities
  • Partner with AI Scientists to build and productionize our tabular foundation models - owning the pretraining, fine-tuning, serving, and monitoring infrastructure and scaling it from research prototype to large-scale production.
  • Build reliable, performant ML infrastructure across research, staging, and production: data, training, and inference pipelines, CI/CD, observability, and reproducible workflows, tuned for latency, throughput, and resource usage.
  • Design evaluation and monitoring that reflect how models are actually used, including handling real-world data challenges such as distribution shift and limited labels.
  • Set engineering standards, lead design and architecture reviews, and drive adoption of new modeling approaches, algorithms, and infrastructure.
  • Partner with product and engineering teams to integrate ML into user-facing systems.
  • Define scope and roadmap for multi-team initiatives, drive cross-team and cross-functional alignment across AI Science, engineering, and product, and mentor senior engineers to raise the organization's technical bar.

Key Qualifications
  • BS in Computer Science, Electrical Engineering, Machine Learning, or a related technical field, plus 6+ years of full-time experience; or MS/PhD plus 4+ years of full-time experience.
  • Strong programming skills in Python with a solid grounding in algorithms and data structures, and deep proficiency with the Python data/ML stack (NumPy, Pandas, scikit-learn, and PyTorch or TensorFlow) for end-to-end model development.
  • 3+ years building production-grade ML infrastructure - data pipelines, training/inference workflows, and deployment automation - with solid software engineering fundamentals (Git, testing, code review, CI/CD, and containerization/orchestration such as Docker and Kubernetes).
  • Track record of shipping ML systems with real attention to scalability, performance, and reliability.
  • Demonstrated technical leadership: owning the roadmap for multi-quarter, multi-team efforts, driving cross-team and cross-functional alignment, and mentoring other engineers.

Preferred Qualifications
  • BS in Computer Science, Electrical Engineering, Machine Learning, or a related technical field, plus 8+ years of full-time experience; or MS/PhD plus 6+ years of full-time experience.
  • Experience pretraining, fine-tuning, and serving foundation models and transformers of any modality (tabular, timeseries, language, vision, etc.) at scale.
  • Experience optimizing training and inference for large-scale models, including distributed/parallel training (multi-GPU/multi-node).
  • Experience deploying ML in production across batch, real-time, or edge settings.
  • Experience with models under real-world data challenges - distribution shift, limited or noisy labels, or continual learning.
  • Development in a cloud environment (AWS, Azure, or GCP).
  • Productive with AI-assisted / agentic coding tools (e.g. Claude Code, Copilot) and eager to push their limits - building workflows and agents that raise the team's velocity.

Apply