{"id":3502931,"url":"https://alion.io/job/clearer-io-senior-aiml-engineer","title":"Senior AI/ML Engineer","company":{"id":1817421,"name":"Clearer.io","domain":"clearer.io","url":"https://alion.io/company/clearer","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ho Chi Minh City, Vietnam","Hanoi, Vietnam"],"countries":["VN"],"hiring_countries":["VN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":54000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Anthropic","optional":true},{"name":"AWS","optional":true},{"name":"AWS Lambda","optional":true},{"name":"CI/CD","optional":true},{"name":"Computer Vision","optional":true},{"name":"Context Engineering","optional":true},{"name":"Copilot","optional":true},{"name":"Cursor","optional":true},{"name":"Databricks","optional":true},{"name":"Datadog","optional":true},{"name":"Docker","optional":true},{"name":"Function Calling","optional":true},{"name":"Hugging Face","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Machine Learning","optional":true},{"name":"MLFlow","optional":true},{"name":"NLP","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenAI Codex","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Qdrant","optional":true},{"name":"Quantization","optional":true},{"name":"RAG","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"Structured Outputs","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-30T02:21:13Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-01T03:24:49Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Engineering has changed. We're changing with it. At clearer.io, we build the software that helps eCommerce merchants win on search, discovery, and customer engagement. Building that well, in this market, means the engineering behind it can't stand still either. AI has moved past being a coding aid, it's reshaping how software actually gets built, from spec to delivery, and we're moving with it rather than waiting for the dust to settle: agentic workflows, new tooling, new ideas about what a software factory even looks like. We build on and around Shopify, a platform that changes just as fast, so staying current isn't a nice to have here, it's the job.\nThat's why we're not hiring for where engineering was. We're hiring the next level of engineer, people who see this pace of change as the interesting part of the work, not something to survive until things settle down.\nYour Impact:\nAs a Senior AI/ML Engineer, you will design, build and run production AI/ML systems, from classic ML models to LLM and agent-based applications, that improve the user experience and business results. You will lead technical work on large AI systems, set a high bar for AI-native engineering practice, mentor engineers and work with stakeholders to define and deliver AI initiatives.\nwhat you'll do\nDevelop and maintain scalable ML pipelines on Databricks and AWS covering data ingestion, feature engineering, model training, evaluation and serving, with monitoring, drift detection and automated retraining.\n\nBuild systems that combine models, tools and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs and failure handling.\n\nIdentify and resolve performance bottlenecks in model training and inference through profiling, optimization, and architectural improvements, optimizing for latency and cost (e.g., TensorRT, quantization, batching).\n\nSet up evaluation frameworks for LLM and ML systems, including offline evals, online A/B tests and quality guardrails, and ensure model fairness, bias mitigation and responsible AI practices.\n\nFind and redesign engineering and business workflows using AI tools, and measure the impact.\n\nCollaborate with cross-functional teams to translate product requirements into production-ready systems, and continuously improve system performance through iteration and evaluation.\n\nMentor engineers on ML practice and AI-assisted development.\n\nwhat you'll bring\n5+ years of software engineering experience with a focus on machine learning systems, model development, or ML infrastructure.\nProven track record of deploying AI/ML solutions at scale in production.\n\nExperience in one or more specialized ML domains: search, recommendation systems, natural language processing (NLP), computer vision or generative AI.\n\nExperience leading and owning end-to-end delivery of AI/ML projects, including technical design, cross-functional collaboration, and operational readiness.\n\nExperience using Cursor, Copilot, Codex or similar AI coding assistants for development, debugging, testing and refactoring.\n\nFamiliarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and speeding up engineering workflows.\n\nHas integrated AI tools to improve engineering efficiency and code quality, or to redesign workflows, with measurable impact such as time saved, defect reduction or throughput gains.\n\nKeeps building AI skills, for example prompt and context engineering or agent orchestration, and stays current with new AI technologies.\n\nExcellent communication and mentorship skills.\n\nGood English communication skills (written and verbal)\nTech Stack:\nLanguages: Python (primary)\n\nData & ML platform: Databricks (Spark, MLflow, Unity Catalog), AWS (SageMaker, S3, Lambda, ECS/EKS)\n\nVector search / RAG: Qdrant\n\nInference optimisation: TensorRT\n\nFrameworks: PyTorch, Hugging Face; LLM APIs (Anthropic, OpenAI) and agent frameworks\n\nNice to have: LLM observability and eval tooling (e.g. Datadog LLM Observability), Docker/Kubernetes, CI/CD for ML\n\nlife at clearer.io\neCommerce is crowded, noisy and increasingly complex. At clearer.io, we're built for a different outcome: clarity you can act on.\nWe connect to the stack a merchant already uses. Our AI agents enrich their catalogue, learn from their reviews and customers, and put that intelligence to work inside the tools they already own - so their products get found, trusted and chosen by shoppers and AI, without replatforming. We do this across our products: Clearer, Boost Commerce, REVIEWS.io and Influence, used by thousands of merchants worldwide.\nWe're a global team with offices in Australia, the UK, Germany, Vietnam and the US, backed for the long term by PSG Equity and Silversmith Capital Partners.\nWe care about doing the work properly: listening first, building second and shipping quickly where it matters. Results matter more than rhetoric.\nWhat guides us day to day:\nCustomers at the heart: we obsess over their needs so we can grow together\n\nPurposeful progress: we don't settle - we take initiative and embrace bold thinking\n\nEndless innovation: we refine, improve, and take meaningful action\n\nAlways open: we build trust through transparency and global collaboration\n\nclear benefits\nEveryone, everywhere\nYour birthday off - a full paid day, just for you, every year\n\nGive Back Day - a paid day each year to volunteer for a cause you care about\n\nBonusly peer recognition - real-time shout-outs tied to our values, with points that convert to real rewards\n\nClearer Together - employees who exemplifies our values wins a fully-funded 5-night trip to one of our global offices (Sydney, Berlin, Leicester, Irvine or Ho Chi Minh City)\n\nStopover - travelling near one of our offices? Once a year we'll fund a day with the local team\n\nVN\n100% Remote Culture: Work from anywhere in Vietnam that brings you happiness.\n\nPerformance-based Year-End Bonus: Recognizing and rewarding your individual contributions.\n\nWellness Allowance: Support for classes promoting physical and mental health.\n\nTime Off: 20 days/year, in addition to Vietnam holidays.\n\nMacBook Provided.\n\nCollaborative Events: Offline meet-ups, monthly gatherings and year-end party.\n\nContinuous Learning: Technical and general workshops, online resources.\n\nHealth and Wellness Benefits: Generali Health Care, annual check-up.\n\nInternational Exposure: Enhance expertise and English communication skills.\n\na fair shot for everyone\nWe're committed to a fair and inclusive hiring experience. Hiring decisions at clearer.io are based on skills, experience and role requirements - nothing else. 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