{"id":843272,"url":"https://alion.io/job/spotter-machine-learning-scientist","title":"Machine Learning Scientist","company":{"id":688593,"name":"Spotter","domain":"spotter.com","url":"https://alion.io/company/spotter-3","size_band":"51-200","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["Culver City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":131000,"max_usd":282000,"period":"year","method":"role_country_seniority_unknown","sample_n":2391},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"bandit","optional":false},{"name":"Embeddings","optional":false},{"name":"Machine Learning","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-06-19T18:32:31Z","employer_posted_date":"2026-08-06","last_verified_at":"2026-09-24T07:25:21Z","board_verified":true,"closed_at":null,"days_open":96,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":96},"description":"Overview\nSpotter empowers the world's best Creators with capital, data, and insights to scale their programming into sustainable media businesses. Through these partnerships, Spotter helps brands partner with creator-led franchises to unlock growth, amplify impact, and build lasting cultural relevance.\nSpotter has already deployed over $1 billion to YouTube Creators to reinvest in themselves and accelerate their growth. With a premium catalog that spans over 725,000 videos, Spotter generates more than 88 billion monthly watch-time minutes, delivering a unique scaled media solution to Advertisers and Ad Agencies that is transparent, efficient, and 100% brand safe. For more information about Spotter, please visit https://spotter.com.\nOverview\nWe're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production machine learning models, particularly reinforcement learning, contextual bandits, and adaptive learning systems, along with deep learning, ranking, personalization, and recommendation systems. You thrive in a fast-paced startup environment and are motivated by building models that don't just perform well in experiments, they ship to production and create real value for YouTube Creators.\nIn this role, you'll train, evaluate, optimize, and deploy a wide range of machine learning models, from contextual bandits and sequential decision-making systems to neural networks, ranking systems, recommendation models, and traditional machine learning approaches. You're passionate about staying at the forefront of AI and machine learning, especially in areas where models learn from feedback, adapt over time, and improve real-world product outcomes.\nWe're a team of builders who value continuous learning, rapid experimentation, and delivering AI solutions that make a measurable difference for Creators. If you enjoy solving complex problems, iterating quickly, and building intelligent products that help the world's top YouTube Creators work smarter and create better content, you'll thrive at Spotter.\nWhat You’ll Do\nYou'll develop machine learning models that move beyond experimentation and into production, where they directly improve Creator workflows and product experiences. Working alongside Analytics, Product, and Engineering, you'll help develop intelligent systems that improve how Creators discover insights, make decisions, and create content.\nYour work may include:\nDesigning, training, evaluating, optimizing, and deploying production reinforcement learning, contextual bandit, and online learning systems that improve product outcomes.\nCreating systems that balance exploration and exploitation, short-term performance and long-term value, and multiple competing product objectives.\nDeveloping reward models, feedback models, and objective functions that translate noisy, sparse, delayed, or implicit signals into reliable model training and evaluation targets, and diagnosing and mitigating reward hacking and feedback loops in deployed systems.\nApplying offline policy evaluation and counterfactual techniques, such as inverse propensity scoring, doubly robust estimation, and replay evaluation, to reason about model changes before and after deployment.\nWorking with logged interaction data to understand user behavior, evaluate model performance, improve decision quality, and reduce bias in model evaluation.\nDesigning experiments to evaluate model performance, measure product impact, and continuously improve production systems.\nBuilding scalable model training, evaluation, deployment, and inference pipelines.\nOptimizing models for accuracy, latency, scalability, reliability, and production maintainability.\nWorking with structured and unstructured datasets using Python and SQL.\nCollaborating closely with Product and Engineering to translate customer problems into machine learning solutions.\nStaying current with advances in reinforcement learning, bandits, recommendation systems, ranking, personalization, deep learning, experimentation, and production ML, and thoughtfully applying new techniques where they create measurable value.\nWho You Are\nRequired Skills & Experience\nMaster's degree or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or another quantitative field.\n5+ years building, evaluating, and deploying machine learning models in production environments.\nExperience with reinforcement learning or contextual bandit systems gained through graduate coursework, academic research, or hands-on industry experience. Candidates with experience building and deploying these systems in production, from problem formulation through offline evaluation to live deployment, are strongly preferred.\nSolid grasp of core RL training objectives and loss functions, including temporal-difference and Bellman error losses (Q-learning, DQN), policy gradient objectives (REINFORCE, actor-critic advantage estimation), and clipped surrogate objectives (PPO, TRPO), with an understanding of when each applies and how they behave in training.\nPractical experience with bandit and reinforcement learning methods such as Thompson sampling, UCB or LinUCB, neural bandits, non-stationary bandits, policy gradients, actor-critic methods, or Q-learning.\nAbility to design reward functions and objective trade-offs for systems optimizing long-horizon outcomes, including diagnosing and mitigating reward hacking and feedback loops.\nKnowledge of off-policy and counterfactual evaluation, such as inverse propensity scoring (IPS), self-normalized IPS, doubly robust estimators, and replay evaluation, and with counterfactual learning from logged bandit feedback, including propensity logging.\nExperience working with logged interaction data, behavioral data, or feedback signals to train, evaluate, and improve models.\nTrack record of designing experiments and using data to improve model performance in real-world product environments, including A/B testing and causal inference.\nStrong experience with modern deep learning frameworks and production ML workflows.\nExpertise in training, evaluating, tuning, and deploying machine learning models across deep learning and traditional ML approaches.\nStrong understanding of embeddings, representation learning, neural networks, sequence modeling, and modern deep learning architectures.\nStrong Python and SQL skills.\nExcellent communication skills and the ability to work cross-functionally with Product, Engineering, Analytics, and other stakeholders.\nCuriosity, ownership, and a passion for building products that customers love.\nNice to Have\nHands-on work building large-scale recommendation, ranking, or personalization systems.\nUnderstanding of offline reinforcement learning methods, such as CQL or IQL, for training policies from logged data.\nKnowledge of constrained or safe reinforcement learning and guardrailed deployment, including offline evaluation gates ahead of live A/B tests.\nFamiliarity 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.\nExperience serving large-scale ML models in production.\nBackground 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 tools.\nWhy Spotter\nBuild AI products used by the world's top YouTube Creators.\nShip production models every week, not every year.\nWork on real-world reinforcement learning, contextual bandit, ranking, recommendation, personalization, and adaptive learning problems.\nBuild systems that learn from feedback, improve over time, and create measurable product impact.\nJoin a small, highly collaborative team where your work has immediate impact.\nHelp shape the future of AI-powered Creator tools.\nMedical insurance covered up to 100%\nDental & vision insurance\n401(k) matching\nStock options\nDiscretionary PTO\nComplimentary gym access\nAutonomy and upward mobility\nDiverse, equitable, and inclusive culture, where your voice matters.\nIn compliance with local law, we are disclosing the compensation, or a range thereof, for roles that will be performed in Culver City. Actual salaries will vary and may be above or below the range based on various factors including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. A reasonable estimate of the current pay range is: $167K-$185K salary per year. The range listed is just one component of Spotter’s total compensation package for employees. Other rewards may include an annual discretionary bonus and equity.\nSpotter is an equal opportunity employer. Spotter does not discriminate in employment on the basis of race, religion, creed, color, national origin, ancestry, citizenship, physical or mental disability, medical condition, genetic characteristics or information, marital status, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, use of or request for family or medical leave, political affiliation, or any other status protected under applicable federal, state or local laws. \nEqual access to programs, services and employment is available to all persons. Those applicants requiring reasonable accommodations as part of the application and/or interview process should notify a representative of the Human Resources Department.","description_format":"text","description_chars":9665,"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":[]},"benefits":["Continuous learning","Equity","Health insurance","Stock options","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Influencers"],"lifecycle":[{"event":"open","at":"2026-09-12T22:56:43Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.269,"p_room":0.28,"age_days":96,"expected_fill_days":43,"reasons":["conf:3","win:tail","crowd:"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/spotter-machine-learning-scientist","json_url":"https://alion.io/job/spotter-machine-learning-scientist.json","meta":{"generated_at":"2026-09-24T07:57:48Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}