Design and develop ML products integrated with Treasure AI’s CDP; own specific technical areas and drive ML product projects with cross-functional teams.
Define system architecture for ML products and implement components to enhance user experience and performance.
Design and implement scalable ELT data pipelines covering the ML lifecycle (training and inference).
Own technical problem solving, mitigate risks, and deliver ML product objectives in ambiguous environments; participate in on-call rotation.
Promote and enforce best practices across ML research methodologies, coding standards, code reviews, source control, testing, release, and operational excellence.
技術スタック
必須スキル
6+ years of software engineering experience building ML‑driven products; at least 3 years in production ML systems
Advanced degree in computer science, data science, ML, or equivalent
Proficiency in Python and ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch)
Experience designing, deploying, and operating scalable ML systems; ML lifecycle management
Data engineering knowledge and experience building ML pipelines
Experience with public cloud services, notably AWS
Strong verbal and written English communication; ability to present findings to technical and non-technical audiences
Ability to collaborate effectively in cross-functional, distributed teams across time zones
歓迎スキル（該当する場合）
Security design principles
Big data technologies (Hive, Trino, Spark, BigQuery, Redshift)
OSS contributions
キャリア成長観点
End-to-end ownership of ML products from concept to production; opportunities to shape architecture and product direction
深いMLライフサイクル、データパイプライン、運用可能なMLシステムの実務経験を獲得
クラウド/データエンジニアリングとML Opsのスキルを深め、グローバルなチームと連携する機会
研究と実装の橋渡し能力を高め、複雑な技術情報を非技術者にも伝えるコミュニケーション力を強化
将来的なキャリアパスとしてStaff/Principal ML EngineerやML Platform leadershipへの道が開かれる可能性