{"id":3740175,"url":"https://alion.io/job/bjak-ai-phone-applied-ml-model-engineer-6","title":"AI Phone - Applied ML / Model 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":"B","score":81,"open_postings":85,"ghost_share":0,"stale_share":0.976,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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":["CN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemma","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Qwen","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Synthetic Data","optional":false},{"name":"AI Agents","optional":true},{"name":"Model Distillation","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-11-04T08:00:18Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-05T18:05:28Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About\nWe are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.\nThere are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.\nResponsibilities\nEvaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.\n\nIdentify which capabilities can be handled by existing models and where additional model adaptation or fine-tuning is needed.\n\nExperiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques for specific use cases.\n\nImprove model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.\n\nExplore synthetic data generation and dataset construction for phone-specific workflows.\n\nBuild evaluation datasets and benchmarks to measure model quality, reliability, and task completion.\n\nCompare different models and approaches, including local versus cloud inference.\n\nWork closely with the Agent Runtime team to improve model performance in actual agent workflows.\n\nRequirements\nStrong background in Machine Learning, Deep Learning, or NLP.\n\nPractical experience working with LLMs and open-weight models.\n\nExperience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.\n\nExperience with PyTorch and common ML tooling.\n\nUnderstanding of LLM evaluation, benchmarking, and dataset preparation.\n\nAbility to run experiments, analyze model failures, and translate findings into measurable improvements.\n\nNice to have\nExperience with agentic systems, function calling, and tool-use training.\n\nExperience with synthetic data generation and preference datasets.\n\nExperience with model distillation and smaller language models.\n\nExperience with deploying models in production 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