{"id":3178121,"url":"https://alion.io/job/lightcast-associate-applied-ml-scientist-1087","title":"Associate Applied ML Scientist - 1087","company":{"id":4679,"name":"Lightcast","domain":"lightcast.io","url":"https://alion.io/company/lightcast","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":92,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Moscow, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":103000,"max_usd":231000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":398},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"BERT","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Sentence-Transformers","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-10-07T17:54:26Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-26T15:29:53Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"Lightcast is seeking an Associate NLP / Machine Learning Scientist to join our AI/ML Laband help develop NLP models for large-scale labor market data.You will work with senior scientists and engineers to build and improve models that transformunstructured data such as job postings and professional profiles into structured labor marketintelligence. Core problem areas include classification, information extraction, entity recognition, normalization, semantic matching, and multilingual NLP.\nMajor Responsibilities:\nDevelop and improve NLP models for classification, extraction, normalization, entity linking, and semantic matching.\n\nTrain and evaluate transformer-based and embedding-based models using architectures such as BERT, DeBERTa, ModernBERT, and Sentence Transformers.\n\nSupport experimentation with LLMs and foundation models, including prompt-based approaches and fine-tuning techniques.\n\nPerform model evaluation, error analysis, and data analysis to identify opportunities for improvement.\n\nHelp optimize models for production with a focus on accuracy, latency, throughput, and cost.\n\nBuild and maintain reproducible training and evaluation pipelines.\n\nWork with multilingual labor market data and support cross-lingual model development.\n\nCollaborate with senior scientists, ML engineers, and data engineers to productionize models.\n\nStay current with advances in NLP, LLMs, embeddings, and machine learning.\n\nEducation and Experience:\nBachelor’s or master’s degree in computer science, machine learning, NLP, data science, or a related field.\n\nStrong foundation in machine learning, natural language processing, and deep learning.\n\nExperience using Python and ML frameworks such as PyTorch, Hugging Face, or scikit-learn.\n\nFamiliarity with transformers, embeddings, classification, information extraction, or entity normalization.\n\nExperience training and evaluating machine learning models, with strong analytical and problem-solving skills.\n\nAbility to collaborate and learn quickly in research and production environments.\n\nInternship, academic, or professional experience building NLP or machine learning models.\n\nFamiliarity with large language models, fine-tuning, LoRA, PEFT, or prompt engineering.\n\nExposure to multilingual NLP, semantic search, retrieval, or entity 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