{"id":105748,"url":"https://alion.io/job/macpaw-ai-research-scientist","title":"AI Research Scientist","company":{"id":40199,"name":"MacPaw","domain":"macpaw.com","url":"https://alion.io/company/macpaw","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kyiv, Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Jira","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"MLX ML","optional":false},{"name":"Model Distillation","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-06-19T08:29:36Z","employer_posted_date":"2026-06-19","last_verified_at":"2026-10-03T08:20:30Z","board_verified":false,"closed_at":null,"days_open":121,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":121},"description":"Jira ticket\nWe’re looking for an AI Research Scientist to join our AI&Research Unit, a team dedicated to pushing the boundaries of fundamental artificial intelligence and bridging the gap between academic advancements and real-world technology.\nOur fundamental research stream focuses on on-device LLM efficiency, deep architectural explorations, and localized machine learning solutions for the macOS ecosystem. It helps uncover high-level utility and solve complex technical challenges directly on user hardware, establishing our technology as a generalized leader in the global market.\nAs an AI Research Scientist, you will take deep ownership of our fundamental research initiatives. You’ll define, test, and coordinate advanced optimization workflows and structural model modifications while driving strategic collaborations with global scientific entities.\nIf you’re excited to lay the foundation for on-device AI efficiency to become a global industry standard, we’d love to hear from you!\nIn this role, you will:\nPrepare fundamental research proposals within our specialized LLM efficiency and optimization streams.\n\nInvestigate new directions in LLM optimization by surveying relevant academic publications, formulating hypotheses, and running deep-dive experiments.\n\nCollaborate closely with internal research scientists and external academic labs on joint research projects and scientific publications.\n\nWork alongside the applied research stream to surface, validate, and transition relevant research prototypes into downstream production environments.\n\nContribute to a strong publication record at top-tier international AI conferences,\n\nTake full ownership of your research niche, driving initiatives from ideation to implementation with a high degree of independence and autonomy.\n\nTake part in internal knowledge-sharing sessions and weekly paper clubs to consistently improve domain expertise.\n\nSkills you’ll need to bring:\nDeep experience in Natural Language Processing (NLP) or a similar machine learning domain, gained through solid academic work, industry experience, or both.\n\nStrong theoretical and practical understanding of recent LLM optimization techniques (ex. quantization, KV-cache compression, speculative decoding, and distillation).\n\nDirect hands-on experience with parameter-efficient fine-tuning (including LoRA and other adapters) and mixture-of-experts (MoE) architectures.\n\nAdvanced prototyping skills and a proven ability to implement complex algorithms and architectures directly from academic papers.\n\nFluent programming capabilities in Python and modern frameworks such as PyTorch, JAX, or TensorFlow.\n\nRobust fundamental knowledge of linear algebra, probability theory, and mathematical statistics.\n\nUpper-intermediate level of English or higher for active scientific writing, international lab collaboration, and conference presentations.\n\nAs a plus:\nA track record of publishing original research at international research conferences in AI/ML/SE/HCI domains.\n\nPractical experience in performance engineering, including code profiling and low-level optimization (GPU, Metal, C++...).\n\nHands-on experience training, running, or deploying localized LLM models on device frameworks like MLX.","description_format":"text","description_chars":3233,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Upper-Intermediate 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