{"id":454038,"url":"https://alion.io/job/sharethis-research-scientist-gen-ai-user-representation-learning","title":"Research Scientist, Gen AI & User Representation Learning","company":{"id":174799,"name":"ShareThis","domain":"sharethis.com","url":"https://alion.io/company/sharethis","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"JobScore","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"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":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":129000,"max_usd":246000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[],"status":"live","first_seen_at":"2026-07-14T19:45:56Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-10-29T06:42:13Z","board_verified":true,"closed_at":null,"days_open":106,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":106},"description":"About Us:\nWe are looking for an exceptional Research Scientist to develop next-generation AI technologies, focusing on user representation learning, semantic understanding, and generative AI applications.\nYou will conduct applied research that advances representation learning, multimodal understanding, and transformer-based modeling while working closely with engineering teams to translate research into production systems. The ideal candidate combines strong scientific thinking with practical engineering skills and enjoys solving challenging problems using large-scale real-world data.\nResponsibilities:\nConduct Applied AI Research\nResearch and develop novel machine learning algorithms for user representation learning, semantic embeddings, and foundation-model applications.\nDesign, prototype, evaluate, and deploy transformer-based generative AI solutions from research through deployment.\nDevelop scalable representation learning techniques using transformers, contrastive learning, self-supervised learning, and retrieval-based architectures.\nInvestigate multimodal learning approaches that jointly model structured, behavioral, textual, and other heterogeneous data.\nBuild Large-Scale AI Systems\nTrain and evaluate models using large-scale behavioral, transactional, social, temporal, and content datasets.\nDesign embedding models, retrieval systems, vector databases, and semantic search pipelines.\nCollaborate with platform and infrastructure engineers to deploy production-quality AI models.\nDesign rigorous offline and online evaluation methodologies and establish reproducible benchmarking pipelines.\nCollaborate Across Teams\nWork closely with product, engineering, and domain experts to identify impactful research opportunities.\nTranslate ambiguous business problems into measurable machine learning objectives.\nCommunicate research findings clearly to both technical and non-technical audiences.\nContribute to the long-term AI research roadmap and technical strategy.\nRequirements:\nEducation\nPhD (completed or near completion) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative discipline.\nEquivalent industrial research experience will also be considered.\nTechnical Expertise\nStrong background in one or more of the following:\nDeep Learning\nRepresentation Learning\nTransformer architectures\nGenerative AI Models\nContrastive Learning\nSelf-supervised Learning\nEmbedding Models\nRetrieval-Augmented Generation (RAG)\nVector Search\nSemantic Search\nInformation Retrieval\nExperience with:\nPython\nPyTorch (preferred) or JAX\nLarge-scale distributed data processing\nModel experimentation and evaluation\nEnd-to-end machine learning system development\nGPU ComputingNVIDIA GPU architecture and CUDA programming fundamentals\nMulti-GPU and distributed training using PyTorch Distributed\nMixed precision training (FP16/BF16/FP8)\nProfiling and optimizing GPU utilization, communication overhead, and training throughput\n\nResearch Mindset\nCandidates should demonstrate:\nStrong scientific rigor\nAbility to establish meaningful baselines before pursuing more complex models\nWell-designed experiments and reproducible evaluations\nData-driven decision making\nIntellectual curiosity and independent problem solving\nWhat We're Looking For:\nCombine research excellence with strong engineering execution.\nEnjoy working with ambiguous, real-world business problems.\nCan independently drive projects from idea to production.\nThrive in highly collaborative, cross-functional environments.\nHave excellent written and verbal communication skills.\nAre passionate about building practical generative AI systems that create measurable business impact.\n5 years of industrial or applied research experience preferred (including internships).\nFor compensation, we set standard ranges based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. 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