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Software Engineer, Machine Learning - US App

• Employment Status: Full-time employee
• Work Hours: Full Flextime (no core time)
• Office: Roppongi, Tokyo

For more details, see the Overview of Our Positions section on our Careers site.

About Mercari
Circulate all forms of value to unleash the potential in all people.

"What can I do to help society thrive with the finite resources we have?" The Mercari marketplace app was born in 2013 out of this thought by our founder Shintaro Yamada as he traveled the world. We believe that by circulating all forms of value, not just physical things and money, we can create opportunities for anyone to realize their dreams and contribute to society and the people around them. Mercari aims to use technology to connect people all over the world and create a world where anyone can unleash their potential. For more information about Mercari Group's mission, see Mercari's Culture Doc.

Organization/Team Mission
Mercari Engineering Principles
Mercari Engineering Principles are a shared understanding that serves as the foundation of engineering beliefs and behavior at Mercari. The Engineering Principles are designed to complement the organizational identity (Mercari's mission, values, and culture) from an engineering viewpoint.

These Principles Ultimately Help Us Achieve Mercari's Mission By Defining The Ideal State We Seek To Realize In The Long Term

• Passion For The Product
• Grow Together
• Solve Through Mechanisms
• Collaborate Openly

For More Details, Please See The Following Link

• Engineering Culture

Our team's mission is to empower Mercari's marketplace with machine learning systems that improve discovery, trust, and ease of use. We build models that drive personalization, ranking, and fraud prevention to enhance the buying experience, while also developing ML systems that simplify the seller journey through solutions such as automated categorization and pricing support. By combining scalable infrastructure, cutting-edge ML techniques, and deep market understanding, we aim to create a seamless and reliable experience for millions of buyers and sellers.

The Machine Learning team is responsible for the end-to-end development and operation of ML pipelines, from data collection and feature engineering to model training, deployment, and monitoring. We ensure our models not only deliver measurable impact on user engagement and conversion, but also operate at scale with stability, fairness, and efficiency.

Recent Or Ongoing Initiatives Include

• Building scalable ML pipelines for ranking, recommendation, and personalization.
• Deploying real-time inference systems that improve user trust and reduce fraud.
• Leveraging large language models (LLMs) and generative AI to enhance search recall, content understanding, and overall user experience.
• Optimizing experimentation frameworks to accelerate product iteration and innovation.
• Collaborating with search, backend, and frontend teams across the US and Japan to deliver high-impact ML features at global scale.

See here for more information about our mission and values.

Work Responsibilities

• Lead the end-to-end ML model lifecycle: independently identify machine learning opportunities through data and metric analysis, define and track KPIs, and ship iterations that align with product and business goals.
• Design, develop, and maintain ML pipelines, including feature engineering, model training, deployment, and monitoring.
• Implement scalable inference services and APIs for real-time and batch predictions.
• Improve model accuracy, inference speed, and robustness through experimentation, hyperparameter tuning, and feature optimization.
• Ensure reliability through comprehensive automated testing, observability, and reproducibility of ML experiments.
• Mentor junior engineers, lead code and model reviews, and actively contribute to architectural decisions and technical documentation.
• Collaborate with cross-functional teams across product, engineering, and operations to deliver impactful ML solutions.

Unique Challenges

• Machine learning is core to Mercari's marketplace, powering search ranking, personalized listing recommendations, and fraud detection which directly impact user trust and growth. You will own the continuous improvement cycle of ML systems, from ideation to production, with measurable business outcomes.
• Collaborate closely with product managers, data engineers, backend engineers, and QA engineers to design advanced ML solutions that balance accuracy, latency, and scalability.
• Architect and operate highly scalable ML services and pipelines to support rapid user and product growth in the US market.
• Proactively contribute to Mercari's engineering culture by sharing knowledge, proposing improvements, and mentoring peers.
• Develop a deep understanding of user behavior and market trends in the US to ensure ML models align with customer expectations and business goals.
• Work in a globally distributed team, collaborating with teammates in both the US and Japan, navigating cultural and time zone differences to deliver high-impact results.

Qualifications

• Required Experience/Skills

• Strong hands-on experience across the machine learning model life cycle: training, deployment, monitoring, and optimization.

• Practical experience leveraging computer vision and natural language processing techniques in production ML systems.
• Ability to independently analyze data and model metrics to ship measurable improvements in production systems.
• Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field (or equivalent practical experience).
• 5+ years of professional experience developing and operating large-scale ML pipelines and/or backend services in high-traffic production environments, including optimizing models for latency, scalability, and cost efficiency.
• Experience with large language models (LLMs) and generative AI, including techniques such as prompt engineering, fine-tuning, vector search integration (RAG), and responsible production deployment.
• Experience with ML frameworks and pipelines (TensorFlow, PyTorch, scikit-learn, MLflow, Kubeflow, or similar).
• Strong programming expertise in Python; familiarity with Go or PHP is a plus.
• Excellent English communication skills, with the ability to collaborate effectively across functions and regions.

• Demonstrated ability to mentor and guide junior engineers.

• Preferred Experience/Skills

• Experience deploying and scaling ML services in production environments, including cloud platforms (GCP, AWS, or Azure), containerization (Docker, Kubernetes), CI/CD, Infrastructure as Code (Terraform), and observability (Prometheus, Grafana).

• Familiarity with data engineering practices, including feature stores, data preprocessing, ETL pipelines, large-scale data management, and real-time streaming or event-driven architectures (e.g., Kafka, Pub/Sub).
• Domain knowledge of marketplace or e‑commerce platforms.
• Contributions to open-source projects in ML or related areas; or public technical engagement through blogs, talks, or conferences.

• Experience working within large, cross-functional, and geographically distributed teams.

• Language

• English: Business level (CEFR B2 or higher) required

• Japanese: Basic (CEFR - A2) optional
• For details about CEFR, see here.

Learn More About Mercari Group

• Careers site:
• Mercan:
• Social media: X / Linkedin

Recruiting at Mercari
At Mercari Group, we value empathizing with and embodying the mission and values of the Group and each company. To promote the creation of an organization that maximizes the total amount of value exhibited by all members, we would like to understand the experience and skills of each candidate as accurately as possible.

Recruiting cycle at Mercari Group

• Application screening
• Skill assessment: For engineering positions, you will be asked to complete a skill assessment on HackerRank or GitHub. For non-engineering positions, you may be asked to complete an assessment depending on the position. (The timing of the assessment may coincide with the interview process.)
• Interview: The number of interviews may vary depending on the position.
• Reference check: We will ask for online references around the timing of the final interview.
• Offer: Offers will be determined carefully in consideration of the final interview and the reference check.

Learn more about our recruiting process here.

Equal Opportunity Hiring
Here at Mercari, we work to realize a world in which no one's potential is limited by their background and everyone has the opportunity to freely create value. We also firmly believe that a mindset of Inclusion & Diversity is essential for us to achieve our mission.

This, of course, extends to our hiring practices as well. Mercari is committed to eliminating discrimination based on age, gender, sexual orientation, race, religion, physical disability, and other such factors so that anyone who shares our mission and values can join us, regardless of their background. For more details, please read our I&D statement.

Please read and acknowledge our Privacy Policy prior to submitting your application.