{"id":1889849,"url":"https://alion.io/job/bjak-senior-machine-learning-engineer-13","title":"Senior Machine Learning 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":"2026-10-07T05:47:15Z"}},"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":[],"countries":[],"hiring_countries":["DE"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":84000,"max_usd":165000,"period":"year","method":"role_seniority_country_cell","sample_n":17},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"JAX","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-09-28T07:06:13Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-08T00:04:53Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"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.\nRole\nAs a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale.\nThis is a hands-on, high-impact role focused on depth.\nFocus\nBuild core ML systems that power a proactive, long-horizon AI product.\n\nOwn work end-to-end: data preparation, training, evaluation, inference, and iteration.\n\nTurn research ideas into working systems that run reliably in production.\n\nDebug model failures and system issues using real production signals.\n\nIterate quickly: ship, measure outcomes, refine, and repeat.\n\nCollaborate closely with research, product, and engineering to deliver real user impact.\n\nMentor and review work from other ML engineers through example and technical judgment.\n\nWork under real production constraints: latency, cost, reliability, and safety\n\nTech Stack\nPython\n\nPyTorch / JAX\n\nGPU-based training and inference systems\n\nIdeal Experience\nYou have built and shipped ML systems used by real users.\n\nYou understand how modern ML models behave - and misbehave - in production.\n\nYou write strong, production-quality code and think in systems, not scripts.\n\nYou take ownership, work independently, and push work across the finish line.\n\nYou learn fast, communicate clearly, and improve through iteration.\n\nOutcomes\nML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.\n\nComplex production issues are monitored, debugged, and resolved with minimal disruption.\n\nTraining, inference, and data pipelines are robust, scalable, and maintainable over time.\n\nDrives measurable improvements in ML systems based on real-world signals and user feedback.\n\nProvides mentorship and technical guidance to peers, raising the overall ML engineering standard.\n\nCollaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.\n\nHow We Work\nThe best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product\nInterview process\nIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.\nApplications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.\nWe value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. 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