{"id":2688045,"url":"https://alion.io/job/bjak-machine-learning-engineer-applied-ai-3","title":"Machine Learning Engineer, Applied AI","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":"B","score":83,"open_postings":81,"ghost_share":0,"stale_share":0.889,"repost_share":0,"time_to_fill_p50_days":9,"computed_at":"2026-10-16T05:45:00Z"}},"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":["London, United Kingdom"],"countries":["GB"],"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":"Embeddings","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Reranking","optional":false},{"name":"JAX","optional":true},{"name":"Machine Learning","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-10-16T07:04:38Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-16T21:06:55Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About\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 first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.\nRole\nBuild and improve ML systems that power our AI-native applications.\nWork on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.\nThis role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.\nFocus\nBuild and improve ML models for classification, ranking, retrieval and personalization.\n\nDevelop efficient classifiers and optimize model accuracy, precision, latency and cost.\n\nImprove search relevance and context retrieval using embeddings, hybrid search and reranking.\n\nBuild personalization models that learn from user behavior while respecting privacy.\n\nDevelop evaluation pipelines, labeled datasets and metrics to measure model quality.\n\nFine-tune and adapt existing models when supported by sufficient data.\n\nBuild and maintain ML training, inference and deployment pipelines.\n\nDebug production issues, monitor model performance and ship improvements based on real-world feedback.\n\nTech Stack\nPython\n\nPyTorch / JAX\n\nEmbeddings, vector search and ranking models\n\nProduction ML systems and inference pipelines\n\nIdeal Experience\nStrong foundations in machine learning and modern neural architectures.\n\nHands-on experience building and deploying ML models in production.\n\nExperience in classification, ranking, retrieval, recommendation systems or personalization.\n\nUnderstanding of model evaluation, experimentation and performance metrics.\n\nComfortable writing production-quality code and working with real-world datasets.\n\nAble to balance model quality, latency, cost and reliability.\n\nStrong ownership, problem-solving skills\n\nOutcomes\nML models in production meet expected accuracy, relevance, latency and reliability targets.\n\nClassification and ranking improvements deliver measurable gains in model quality.\n\nEvaluation pipelines identify regressions and support reliable model releases.\n\nTraining, inference and data pipelines are robust, reproducible and maintainable.\n\nModel improvements are driven by real-world feedback and measurable results.\n\nCollaborates effectively with engineering and product teams to deliver reliable ML-powered features.\n\nHow We Work\nThe best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.\nJoining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.\nInterview Process\nIf there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.\nApplications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite.\nWe value transparency and efficiency, so expect a prompt decision. 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