{"id":2023874,"url":"https://alion.io/job/spotter-machine-learning-scientist-2","title":"Machine Learning Scientist","company":{"id":3202344,"name":"Spotter","domain":"spotter.la","url":"https://alion.io/company/spotter-la","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Culver City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":167000,"max":185000,"currency":"USD","period":"year","gross":null,"usd_annual":185000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Embeddings","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-07-02T00:16:05Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-11T03:32:55Z","board_verified":true,"closed_at":null,"days_open":101,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":100},"description":"Join our team as a Machine Learning Scientist, where you'll leverage your expertise in building and deploying production machine learning models to create real value for YouTube creators. You'll work in a fast-paced startup environment, training, evaluating, optimizing, and deploying a wide range of machine learning models. Your passion for AI and machine learning will drive you to stay at the forefront of the field and continuously improve our products.\nMissions\nDesigning, training, evaluating, optimizing, and deploying production machine learning models, particularly in areas such as deep learning, reinforcement learning, and recommendation systems.\nBuilding recommendation, ranking, and personalization systems that adapt to creator behavior, product feedback, and changing objectives.\nCollaborating closely with Product and Engineering to translate customer problems into machine learning solutions, and staying current with advances in AI and machine learning.\nProfil recherché\n - Curiosity, ownership, and a passion for building products that customers love- Experience training, evaluating, tuning, and deploying machine learning models across deep learning and traditional ML approaches\n- Experience with offline evaluation, A/B testing, counterfactual reasoning, causal inference, or other methods for measuring model impact\n- Reinforcement learning systems\n- Excellent communication skills and the ability to work cross-functionally with Product, Engineering, Analytics, and other stakeholders\n- Strong understanding of embeddings, representation learning, neural networks, sequence modeling, and modern deep learning architectures\n- Strong experience with modern deep learning frameworks and production ML workflows\n- Strong understanding of reinforcement learning concepts such as exploration vs. exploitation, reward design, policy evaluation, delayed feedback, feedback loops, and sequential decision-making\n- Recommendation systems\n- Experience designing experiments and using data to improve model performance in real-world product environments\n- Strong Python and SQL skills\n- Contextual bandits\n- Adaptive decision-making systems\n- Online learning systems\n- 5+ years building, evaluating, and deploying machine learning models in production environments\n- Personalization models\n- Ranking systems\n- Experience building one or more of the following:\n- Experience working with logged interaction data, behavioral data, or feedback signals to train, evaluate, and improve models\n- Master’s degree or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or another quantitative field\n- Experience with large-scale recommendation, ranking, personalization, or adaptive optimization systems\n- Familiarity with ad recommendation, ad ranking, or campaign optimization systems used by large-scale platforms, such as YouTube, Google, Meta, TikTok, Amazon, or similar consumer marketplace platforms\n- Experience serving large-scale ML models in production\n- Experience building machine learning systems for large-scale digital platforms, such as creator platforms, consumer apps, recommendation systems, ad recommendation systems, campaign optimization systems, or workflow automation 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