Software Engineer, Machine Learning - US App
Salary not provided
Minimum year of experience: 5
Mercari
Software Engineer, Machine Learning — US App
- Employment Status: Full-time employee
- Work Hours: Full Flextime (no core time)
- Office: Roppongi, Tokyo
Team Mission
Our mission is to empower the marketplace with machine learning systems that improve discovery, trust, and ease of use. We build models for personalization, ranking, fraud prevention, and seller support (e.g., categorization, pricing). By combining scalable infrastructure, cutting-edge ML, and deep market understanding, we aim for a seamless experience for millions of users.
The Machine Learning team is responsible for the entire ML pipeline: data collection and feature engineering, model training, deployment, and monitoring. We ensure models deliver measurable impact on engagement and conversion—all at scale, with stability, fairness, and efficiency.
Recent Initiatives:
- Building scalable ML pipelines for ranking, recommendation, personalization
- Deploying real-time inference systems to improve trust and reduce fraud
- Leveraging LLMs and generative AI for search, content understanding, UX
- Optimizing experimentation frameworks to accelerate innovation
- Cross-functional collaboration with teams in the US and Japan
Work Responsibilities
- Lead end-to-end ML model lifecycle: identify opportunities, analyze data, track KPIs, and deliver iterations aligned with goals
- Design, develop, and maintain ML pipelines (feature engineering, model training, deployment, monitoring)
- Implement scalable inference services and APIs (real-time and batch)
- Improve accuracy, inference speed, and robustness through experimentation and optimization
- Ensure reliability with automated testing, observability, and reproducibility
- Mentor junior engineers, conduct reviews, contribute to architecture and documentation
- Collaborate with product, engineering, and operations teams for impactful ML solutions
Unique Challenges
- Own improvement cycles for ML systems critical to search, recommendations, and fraud detection, directly impacting user trust and growth
- Collaborate cross-functionally to design advanced ML balancing accuracy, latency, scalability
- Architect and operate scalable ML services and pipelines for fast-growing US market
- Actively shape engineering culture via knowledge sharing and mentoring
- Develop user and market insights to align models with business goals
- Work with distributed teams across the US and Japan
Qualifications
Required Experience/Skills
- Strong experience in complete ML model lifecycle: training, deployment, monitoring, optimization
- Practical production experience in computer vision and NLP
- Independent data and metrics analysis for measurable production improvements
- Bachelor’s degree in Computer Science, Data Science, Mathematics, or related; or equivalent experience
- 5+ years in large-scale ML pipelines or backend services in high-traffic environments; optimizing for latency, scalability, cost
- Experience with LLMs and Generative AI (prompt engineering, fine-tuning, RAG, responsible deployment)
- Familiarity with ML frameworks/pipelines (TensorFlow, PyTorch, scikit-learn, MLflow, Kubeflow, etc.)
- Strong Python skills; Go or PHP a plus
- Excellent English communication for cross-functional/cross-regional collaboration
- Track record mentoring and guiding junior engineers
Preferred Experience/Skills
- Deploying/scaling ML services in production: cloud (GCP, AWS, Azure), containers (Docker, Kubernetes), CI/CD, IaC (Terraform), observability (Prometheus, Grafana)
- Data engineering (feature stores, ETL, large-scale data management, streaming/event-driven architectures like Kafka, Pub/Sub)
- Marketplace or e-commerce domain knowledge
- Open-source project contributions or public technical engagement (blogs, talks, conferences)
- Experience in large, distributed, cross-functional teams
Language
- English: Business level (CEFR B2 or higher) — required
- Japanese: Basic (CEFR A2) — optional
Learn More
- Careers site
- Mercan
- Social: X / LinkedIn
Recruiting Process
- Application screening
- Skill assessment: For engineering positions, via HackerRank or GitHub
- Interview: Number varies by position
- Reference check: At final interview stage
- Offer: After consideration of interview and references
Equal Opportunity Hiring
We are committed to eliminating discrimination based on age, gender, sexual orientation, race, religion, disability, and more, so anyone who shares our mission and values can join us, regardless of background. For more, read our Inclusion & Diversity statement.
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