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機械学習エンジニアの求人266件応募資格・募集要項・仕事内容を見る
Product Engineer (AI/ML), CS & TnS ML
- 給与
- 記載なし
- 勤務地
- 記載なし
- 休日
- 記載なし
- 雇用形態
- 正社員
どんな仕事かAIによる生成
The CS & TnS ML team builds machine learning models to keep Mercari safe and reduce manual operations across moderation, customer support, and payment fraud prevention. This role owns ML problems end to end, from specification, training, and evaluation through production serving, monitoring, and improvement.
この求人に応募
応募資格
必須
- Proficiency in Python.
- Solid foundation in machine learning and deep learning, with hands-on experience training, evaluating and debugging models (work, research or personal projects).
- Daily use of AI coding tools in your development work, with the habit of reviewing and verifying what they produce.
- English: Proficient (CEFR B2). Required.
- Japanese: Basic (CEFR A2). Required.
歓迎
- 2+ years of professional experience in machine learning or software engineering, with models in production.
- Experience in one or more of: computer vision, NLP (Japanese is a plus), fraud or anomaly detection on tabular and sequence data (for example gradient boosting, feature engineering), agentic AI.
- Experience serving models in production: Docker, Kubernetes, gRPC or REST, model optimization for latency (for example ONNX, TensorRT, quantization).
- Experience with cloud and data tools: GCP, SQL and BigQuery, and ML pipelines (for example Kubeflow, Vertex AI, Apache Beam).
- Experience designing offline evaluations and A/B tests, and monitoring models in production.
- Knowledge of trust and safety, content moderation or fraud prevention.
- Master's or Ph.D. in a related technical field, or the habit of following new research and applying it.
求める人物像
- Shared belief in the mission and values of Mercari Group.
- Eagerness to learn quickly, take end-to-end ownership, and work beyond the boundary of your own area of expertise.
- Strong written and verbal communication, with the ability to propose ideas and align stakeholders (product, operations, policy).
募集要項最終確認 11/1
- 職種
- 機械学習エンジニア
- 雇用形態
- Employment Status: Full-time
- 給与
- 記載なし
- 勤務地
- Roppongi
- 働き方
- 記載なし
- 勤務時間
- Full Flextime (no core time)
- 学歴
- 大学院修了以上
- 選考の流れ
- Application screeningSkill 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.
- 掲載日
- 2026/10/29 21:58（4日前）
- 最終確認
- 2026/11/01 20:10（1日前）
仕事内容
CS & TnS ML team
- The Customer Support (CS) & Trust and Safety (TnS) Machine Learning (ML) Team keeps Mercari safe for tens of millions of users. We build machine learning models that keep the marketplace safe and reduce manual operations, by detecting prohibited items, harmful behavior, fraud and abuse, and by automating replies to customers. Our models run in real time and decide, within milliseconds, what is safe to show or complete. We own the full life of each model: from the first specification, through training and evaluation, to serving, monitoring and improvement in production.
Work Responsibilities
- End-to-End Ownership: Own an ML problem from specification to delivery. Work with product and operation teams to turn a rule or a risk into a measurable target. Then build, ship and maintain the model that meets it.
- Model Development: Design, train and evaluate models on image, text and behavioral data (multimodal deep learning, Japanese NLP, tabular and sequence models). Apply them across our domains: item moderation, comment moderation, transaction message moderation, customer auto-reply, and payment fraud prevention.
- Production Serving: Deploy models as reliable, low-latency real-time services (for example ONNX, TensorRT, gRPC on Kubernetes). Build the CI/CD, rollout and rollback paths. Monitor model health, latency and cost.
- Evaluation and Experimentation: Build offline evaluation that matches production conditions. Verify changes with shadow runs and A/B tests. Detect drift and quality regressions early, with dashboards and alerts.
- Data and Labels: Improve the data behind each model. Work with moderators, customer reports and labeling tools. Find and handle label noise, bias and gaps.
- Initiative and Proposals: Find where ML can reduce risk or manual work, propose the initiative with a clear business case, and drive it to delivery.
- Rapid Response: Respond quickly to urgent requests, such as a viral incident or a policy change. Deliver a safe first fix fast, then improve it.
- Working Across Boundaries: Do what the problem needs, not only what your title says. This can include data pipelines, backend services and infrastructure. Use AI coding tools to move faster, and stay responsible for the quality of the result.
Unique Challenges
- Rare and Noisy Positives: Harmful content is a tiny share of everything on the marketplace. You will build models and evaluations that stay trustworthy under extreme class imbalance and label noise.
- Fast-Moving Threats and Urgent Requests: Abusers change tactics, a post can go viral, and policies change. You will adapt models and thresholds quickly, without hurting precision for the many legitimate users.
- Real-Time at Scale: Our decisions must come within strict latency and cost limits for tens of millions of users. You will choose and tune methods that are accurate and also fast enough to serve.
- Many Domains, One Platform: Items, comments, messages, payments and customer support each have their own data and risks. You will share methods, tooling and serving across them, and learn new domains as the need appears, with support from senior teammates.
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