{"id":3425492,"url":"https://alion.io/job/featherless-ai-ai-researcher-training-optimization","title":"AI Researcher — Training Optimization","company":{"id":4483944,"name":"Featherless AI","domain":null,"url":"https://alion.io/company/featherless-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"worldwide","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"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":"arXiv","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"FSDP","optional":true}],"status":"live","first_seen_at":"2026-09-25T01:06:35Z","employer_posted_date":null,"last_verified_at":"2026-09-25T01:06:35Z","board_verified":false,"closed_at":null,"days_open":34,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":34},"description":"About the RoleWe’re looking for an AI Researcher focused on training optimization to help us push the efficiency, stability, and scalability of large-scale model training. You’ll work at the intersection of research and systems, developing novel techniques to reduce training cost, accelerate convergence, and improve model quality—while validating ideas through rigorous experiments and publications.\nThis role is ideal for someone who enjoys turning research insights into practical training wins, and who has a track record (or strong ambition) of publishing applied ML research.\nWhat You’ll Work On\nDesign and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies)\n\nImprove training efficiency and stability across long runs and large datasets\n\nResearch and implement methods such as:\n\nOptimizer and scheduler innovations\n\nMixed-precision, low-precision, and memory-efficient training\n\nGradient noise reduction, scaling laws, and convergence analysis\n\nTraining-time regularization and robustness techniques\n\nRun large-scale experiments, analyze results, and translate findings into actionable improvements\n\nAuthor or co-author research papers, technical reports, or blog posts\n\nCollaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance\n\nWhat We’re Looking For\nStrong background in machine learning research, with emphasis on training dynamics and optimization\n\nExperience training large neural networks (LLMs, multimodal models, or large sequence models)\n\nPublication experience in ML venues (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv) or equivalent high-quality open research\n\nSolid understanding of:\n\nOptimization theory and practice\n\nBackpropagation, gradient flow, and training stability\n\nDistributed and large-batch training\n\nProficiency in Python and modern ML frameworks (PyTorch preferred)\n\nAbility to independently design experiments and reason from data\n\nNice to Have\nExperience with non-standard architectures (e.g. RNN variants, long-context models, hybrid systems)\n\nExperience optimizing training on GPUs at scale (FSDP, ZeRO, custom kernels)\n\nContributions to open-source ML or research codebases\n\nComfort operating in fast-moving, ambiguous startup environments\n\nWhy This Role\nReal influence over core model training decisions\n\nFreedom to pursue and publish novel research\n\nDirect access to large-scale experiments and real production constraints\n\nA small, senior team that values thinking deeply and shipping thoughtfully\n\nOriginally posted on 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