{"id":2774811,"url":"https://alion.io/job/roms-machine-learning-engineer-english","title":"Machine Learning Engineer (English)","company":{"id":2675594,"name":"ROMS","domain":"roms.inc","url":"https://alion.io/company/roms","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HERP","truth_index":{"grade":"D","score":46,"open_postings":10,"ghost_share":0.9,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tokyo, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"DynamoDB","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"MySQL","optional":true},{"name":"PostgreSQL","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2022-11-29T09:55:00Z","employer_posted_date":"2022-11-29","last_verified_at":"2026-10-20T07:20:17Z","board_verified":true,"closed_at":null,"days_open":1420,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":1420},"description":"Machine Learning Engineer (English)\n募集概要\n# ROMSについて\nROMSは、物流倉庫/工場向けに、高性能小型自動倉庫を中心とした自動化ソリューションを提供しているロボティクススタートアップです。（※より詳細の情報は[弊社コーポレートサイト](https://roms.inc/recruit)よりご確認いただけます）。\n倉庫や工場などの現場では、人手不足、EC需要の拡大、出荷量の変動、限られたスペースの有効活用といった課題が深刻化しています。\n一方で、現在の自動倉庫市場は、大手企業が数億〜数十億円規模の大型設備で席巻しており、本来自動化を必要とする中堅・中小企業には手が届きませんでした。ROMSは、この構造そのものを変えるべく2019年に創業したスタートアップです。\n祖業である超小型無人店舗の開発で培った小型化設計技術・知能化搬送技術を核に、「日本製・フル内製」にこだわり抜いた自動倉庫システムを展開しています。スモールスタートを可能にした国産自動倉庫システムで、これまで自動化の対象とされてこなかった中小規模の現場からでも導入できる自動倉庫として、業界に新しい選択肢を提示しています。\n2023年に物流倉庫の自動化領域へ本格展開を開始。セイノーグループ様、NTTロジスコ様をはじめとする国内大手物流事業者様への導入が進み、現在数十拠点で稼働中。「デモではなく、稼働する収益資産」として、実際の物流現場で価値を証明し続け、製造メーカー様の工場や倉庫への導入も進んでいます。\n2025年以降は米国市場にも進出し、ROMS USA, Inc.（デラウェア州）を設立。米国での導入案件も進行中です。国内では大手マテハンメーカー・システムインテグレーターとの協業関係も構築し、国内展開をさらに加速しています。\nさらに、2026年8月には[自律昇降ロボットによる次世代型自動倉庫（NINBUS）](https://youtu.be/mA5AunG32Ek?si=cVYSvQdPftvVoopJ)を発表、ボット一台からスタートできる自動倉庫として、不定形な空間・多様なレイアウトへの対応領域を広げています。\n＜物流/製造向けプロダクト一覧＞\n* 3次元走行型自動倉庫：[NINBUS](https://roms.inc/product/ec-logistics/ninbus)（[動画](https://youtu.be/mA5AunG32Ek?si=sFzKWKM%5FizQbQJ0S)）\n* 自動倉庫：[Nano-Stream](https://roms.inc/product/ec-logistics/nano-stream)（[動画](https://youtu.be/XI3-EYQQMWg?si=7HzTRBUWsE8QC66r)）\n* 高層ピース仕分け機：[Nano-Sorter](https://roms.inc/product/ec-logistics/nano-sorter)（[動画](https://youtu.be/LamIB5FIb68?si=j5wLG9SSvdWK9KM0)）\n* SaaS：[梱包アシストAI](https://roms.inc/product/ec-logistics/packing-assist-ai)\n## About ROMS\nThank you for your interest in ROMS.\nFirst, please check our company and business profile on our corporate website.\nhttps://roms.inc/\nWe are a start-up company established in 2019, planning, developing, and selling high-performance small automated warehouse solutions and robot picking systems for e-commerce/retail/logistics/manufacturers.\nAlthough we started with the development of ultra-compact autonomous stores in the 30㎡ class and, utilizing its elemental technologies, we have recently developed the industry's most efficient compact automated warehouse system for distribution centers and factories, as well as 3D high-speed piece sorting machines and robot picking systems, all in-house.\nIn September 2022, we opened an unmanned store for quick commerce, RCS (Robotics Convenience Store), in Shibuya with KDDI and completed a total of 1.2 billion yen in funding. In February 2023, we opened a laboratory in Heiwajima and started expanding to the area of warehouse automation for logistics, e-commerce, and manufacturers.\nWe already plan to implement our solutions for several companies in Japan and expand our business overseas in the next 1-2 years. We are also developing new products to accelerate market penetration, which we plan to actively promote at exhibitions and seminars this year.\n## Why ROMS?\n* Build a platform that modernizes warehouse and logistics operations through robotics, automation, data, and AI-helping customers improve throughput, accuracy, and operational efficiency.\n* Learn from teammates who have led large-scale system development in major manufacturing and enterprise environments.\n* Join from the planning stage and own the full lifecycle: problem discovery → design → development → evaluation → rollout and iteration.\n* Work cross-functionally with product, business, and hardware/automation teams as needed to ensure AI solutions fit real-world operational constraints and deliver measurable impact.\n* Move fast with high ownership: we build, ship, learn from customer feedback, and improve in short cycles.\n## Job Description\nRole summary：\nAs an AI/ML Engineer on our Data Science team, you will lead the development of AI models, analytics, proposals, and tools that improve warehouse and logistics operations for customers.\nOur data science work falls into two categories:\n* Operational optimization for ROMS automation solutions like NFC / Nano-Stream / Nano-Sorter / RCS (e.g., inventory management, workload/demand forecasting, operational planning, decision support)\n* Standalone AI software products, such as Packing Assistant AI\nData you will handle (examples)：\n* Real-time operational data with 10,000+ orders per day\n* Real-time inventory and stock movement data\n* Shipping and fulfillment data (picking/packing/shipping events)\nWhat you will work on：\n* ML (tabular & time series): forecasting, classification, anomaly detection, recommendations\n* Optimization: planning/allocation (e.g., packing-related optimization, inventory allocation)\n* LLM-based AI: multi-agent workflows, RAG, and automation of operational decision-making\n* Descriptive analytics / BI: KPIs, dashboards, root-cause analysis, reporting\n## Responsibilities\n* Build analytics, decision-support tools, and AI/ML services that improve warehouse operations\n* Design, develop, evaluate, and operate related systems and internal tools in production\n* Collaborate with cross-functional teams (Business Development, Automation/Robotics engineers etc.)\n* Contribute to team development (ways of working, hiring/interviews, mentoring)\n## Tech stack / Development environment\nWe focus on solid engineering fundamentals and production-ready delivery. Specific tools may vary by project; examples below reflect our typical stack.\n* Core ML / DS: Python; common ML frameworks such as scikit-learn and deep learning frameworks (PyTorch / TensorFlow)\n* Data & Analytics: SQL databases (e.g., PostgreSQL, MySQL); NoSQL databases (e.g., DynamoDB, MongoDB); BI / visualization tools (any major platform); analytics warehouses / OLAP (e.g., Redshift)\n* Data Engineering / Orchestration: workflow/DAG orchestration (e.g., AWS Step Functions, Apache Airflow); building and operating data pipelines; streaming/messaging (e.g., Kinesis, Kafka)\n* Cloud / Platform: we primarily use AWS, but experience with GCP/Azure is also welcome\nEngineering & Collaboration: containerization and CI/CD practices (e.g., Docker); version control and documentation tools (e.g., GitHub)\n## Skills\n### Required\n* Strong ownership mindset: you proactively build, improve, and take initiative\n* 3+ years of hands-on software development experience in Python (or equivalent), including production-grade implementation\n* Practical experience applying ML/AI libraries to solve real business problems\n* Interest in logistics / warehouse operations and automation-driven workflows (retail domain knowledge is a plus)\n### Welcomed\n* Experience building optimization models and applying them to real operations\n* Experience building LLM-based solutions (evaluation, RAG, agents/multi-agent workflows, and production guardrails)\n* Experience with BI tools and communicating insights through dashboards/visualizations\n* Experience with workflow orchestration and scalable data pipelines (e.g., Step Functions, Airflow)\n* Experience operating data/ML systems on cloud platforms (we mainly use AWS)\n### Language / Team\nWe are an international team.\nThe company’s primary language is Japanese, while our team uses a lot of English in day-to-day work.\nCommunication is often mixed Japanese/English since not everyone is fluent in both.\nYou should be fluent in either Japanese 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