{"id":603652,"url":"https://alion.io/job/mercari-ml-operations-engineer-aillm-mercari","title":"ML Operations Engineer (AI/LLM) - Mercari","company":{"id":914,"name":"Mercari","domain":"mercari.com","url":"https://alion.io/company/mercari","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":87,"open_postings":120,"ghost_share":0.142,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":92,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Minato, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Bigtable","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Terraform","optional":false},{"name":"TPU","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"GitHub","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"RAG","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-08-28T00:00:00Z","employer_posted_date":"2026-08-28","last_verified_at":"2026-11-02T07:01:01Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":66},"description":"本ポジションは日本語JDの用意がありません。\nML Operations Engineer (AI/LLM) - Mercari\nEmployment Status: Full-time\nWork Hours: Full Flextime (no core time) \nOffice: Roppongi \nFor more details, see the Overview of Our Positions section on our Careers site.\nAbout Mercari\nCirculate all forms of value to unleash the potential in all people\n\"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\nOrganization/Team Mission\nMercari Engineering Principles\nMercari 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.\nThese principles ultimately help us achieve Mercari’s mission by defining the ideal state we seek to realize in the long term.\nPassion For The Product\nGrow Together\nSolve Through Mechanisms\nCollaborate Openly\nFor more details, please see the following link:\n Engineering Culture\nThe AI / LLM Team’s mission is focused on three core pillars, “product”, \"enablement\" and \"research\", delivering new AI-driven features and user experiences to maximize product-facing impact for Mercari's business. We do this both through independent initiatives owned by our team, as well as by horizontally collaborating with product, engineering, and research teams across the entire organization.\nAs an MLOps engineer on the AI/LLM team, you will own how our machine learning and LLM models reach production and stay healthy there in our cloud-native environment. Your focus is the production serving, deployment, and operations that turn models into reliable, cost-efficient services, seamlessly integrating with our machine learning operations to serve tens of millions of users.\nWork Responsibilities\nData and Model Orchestration: Own the end-to-end orchestration of model inference, including integrating with DataServ for retrieval (BigQuery, BigTable, Valkey) and managing the Model Inference Gateway and Console.\nModel Serving and Deployment: Own production model serving on the cloud-native NVIDIA and TPU stacks (Triton Inference Server, TensorRT-LLM, JAX/TPU Gateways). Manage model repositories, dynamic batching, and concurrent model execution. Build CI/CD, rollout, and rollback paths for safe, scalable model deployment, and automate provisioning and lifecycle management (Terraform, Kubernetes) so the platform scales seamlessly across teams.\nInference Performance Optimization: Profile and optimize deployments across LLM and non-LLM workloads, including model compilation, quantization, and batching strategies to hit latency and throughput targets while managing cost. Maintain performance baselines and regression detection.\nMonitoring and Reliability: Build robust monitoring and alerting for model health and latency, including service-level metrics for the data retrieval and inference gateway layers. Define SLOs and own on-call and incident response for the serving layer.\nModel Quality and Evaluation: Build automated evaluation and quality monitoring into the deployment path, regression and drift detection, offline/online evaluation, and LLM output quality checks, so models stay healthy long after launch.\nResearch-to-Production Enablement: Partner with ML engineers and researchers to turn experimental models into production-ready services, providing self-service workflows and abstractions that let teams deploy safely and quickly without deep infrastructure expertise.\nUnique Challenges\nBuild and operate the production ML serving and Data Orchestration platform behind Mercari Group's AI and LLM features, serving tens of millions of users.\nDrive the strategy for model inference at scale, bridging the gap between complex data retrieval and fast-moving ML model inference to ensure high-performance, cost-effective service delivery.\nShape Mercari's next-generation LLM serving stack, from inference optimization (quantization, dynamic batching, KV caching) to the evaluation and execution infrastructure needed for emerging agentic AI workloads.\nQualifications\nRequired Experience/Skills\nShared belief in the mission and values of Mercari Group.\nBachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.\n5+ years of software engineering experience, including proven experience in production MLOps: end-to-end model deployment, serving, and CI/CD in cloud environments.\nExperience designing and operating large-scale, high-availability distributed systems, including observability, SLO definition, and incident response.\nStrong experience in cloud-native infrastructure (Kubernetes, Docker).\nProficiency in Python and infrastructure-as-code (Terraform).\nExcellent written and verbal communication.\nPreferred Experience/Skills\nExperience integrating ML serving with large-scale distributed data layers (e.g., data warehouses, wide-column stores, in-memory caches).\nExpertise in model inference optimization (TensorRT-LLM, quantization, JAX).\nExperience operating large-scale model inference gateways and orchestrators.\n2+ years of hands-on experience operating GenAI/LLM workloads in production (e.g., LLM serving frameworks, token throughput and cost optimization).\nExperience building LLM evaluation, guardrail, or quality-monitoring pipelines (e.g., LLM-as-judge, golden datasets, drift detection).\nExperience with serving infrastructure for RAG or agentic AI workloads (vector search, tool-calling execution environments).\nExperience partnering closely with research or data science teams to bring research innovations into production.\nMaster's or Ph.D. in a related technical field.\nLanguage \nEnglish: Proficient (CEFR - B2)\nJapanese: Independent (CEFR - B2) optional\nFor details about CEFR, see here.\nLearn More About Mercari Group\nCareers site: https://careers.mercari.com/en/ \nMercan: https://mercan.mercari.com/en/ \nSocial media: X / Linkedin \nRecruiting at Mercari\nAt 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.\nRecruiting cycle at Mercari Group\nApplication screening\nSkill 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.)\nInterview: The number of interviews may vary depending on the position.\nReference check: We will ask for online references around the timing of the final interview.\nOffer: Offers will be determined carefully in consideration of the final interview and the reference check.\nLearn more about our recruiting process here.\nEqual Opportunity Hiring\nHere 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.\nThis, 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. 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