{"id":1889825,"url":"https://alion.io/job/bjak-machine-learning-platform-engineer-2","title":"Machine Learning Platform Engineer","company":{"id":12599,"name":"Bjak","domain":"bjak.my","url":"https://alion.io/company/bjak","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":100,"open_postings":80,"ghost_share":0,"stale_share":0.05,"repost_share":0,"time_to_fill_p50_days":8,"computed_at":"2026-10-06T05:45:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seoul, South Korea"],"countries":["KR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":70000,"max_usd":209000,"period":"year","method":null,"sample_n":5136},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"JAX","optional":true},{"name":"LLM","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"SGLang","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-28T07:07:21Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-06T21:52:04Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"About ActAI\nThere are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.\nOur product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.\nOur objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.\nAbout the Role\nAs an ML Platform Engineer, you will build the infrastructure and systems that power ActAI's AI capabilities.\nYou will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.\nYou will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.\nFocus\nBuild and operate the ML infrastructure and platforms powering A1’s AI products\n\nDesign systems for model training, evaluation, deployment, inference, and experimentation\n\nBuild and optimise model serving and inference infrastructure for high-throughput and low-latency workloads\n\nImprove reliability, scalability, latency, and cost efficiency of AI systems\n\nDevelop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement\n\nBuild platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster\n\nDevelop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions\n\nBuild production observability, monitoring, tracing, and alerting for AI/ML workloads\n\nImprove AI systems across reliability, scalability, latency, throughput, and cost\n\nIdentify bottlenecks across the ML stack and continuously improve system performance\n\nWork closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure\n\nTech Stack\nPython\n\nPyTorch / JAX\n\nLLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM\n\nCloud infrastructure\n\nDistributed systems\n\nML/data pipelines and workflow orchestration\n\nGPU infrastructure and performance tooling\n\nVector databases and retrieval infrastructure\n\nIdeal Experience\nStrong software engineering fundamentals and experience building production systems\n\nExperience building ML infrastructure, platforms, or production machine learning systems\n\nExperience with model deployment, inference, evaluation, or data pipelines\n\nStrong understanding of distributed systems and system reliability\n\nAbility to write clean, maintainable, production-quality code\n\nComfortable working in ambiguous, fast-moving environments\n\nBias toward ownership, experimentation, and continuous improvement\n\nOutcomes\nAI infrastructure reliably supports production workloads at scale\n\nModels can be trained, evaluated, deployed, and improved efficiently\n\nInference systems deliver strong latency, throughput, reliability, and cost efficiency\n\nML pipelines are reproducible, observable, maintainable, and robust\n\nModel and infrastructure regressions are detected quickly and diagnosed efficiently\n\nCommon ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product\n\nThe AI stack can evolve rapidly as new models, architectures, and inference techniques 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