Machine Learning Scientist

Spotter•Culver City, CA

7h•$167,000 - $185,000

About The Position

Spotter 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. Spotter has already deployed over $980 million to Creators to reinvest in themselves and accelerate their growth, with plans to reach $1 billion in investment in 2026. 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. We’re looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production machine learning models, particularly in areas such as deep learning, reinforcement learning, contextual bandits, ranking, personalization, recommendation systems, and adaptive learning 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. In this role, you’ll train, evaluate, optimize, and deploy a wide range of machine learning models, from neural networks and ranking systems to contextual bandits, recommendation models, sequential decision-making systems, 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. We’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.

Requirements

Master’s degree or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or another quantitative field.
5+ years building, evaluating, and deploying machine learning models in production environments.
Strong experience with modern deep learning frameworks and production ML workflows.
Experience building one or more of the following: recommendation systems, ranking systems, personalization models, reinforcement learning systems, contextual bandits, online learning systems, adaptive decision-making systems.
Strong understanding of reinforcement learning concepts such as exploration vs. exploitation, reward design, policy evaluation, delayed feedback, feedback loops, and sequential decision-making.
Experience working with logged interaction data, behavioral data, or feedback signals to train, evaluate, and improve models.
Experience designing experiments and using data to improve model performance in real-world product environments.
Experience with offline evaluation, A/B testing, counterfactual reasoning, causal inference, or other methods for measuring model impact.
Experience training, evaluating, tuning, and deploying machine learning models across deep learning and traditional ML approaches.
Strong understanding of embeddings, representation learning, neural networks, sequence modeling, and modern deep learning architectures.
Strong Python and SQL skills.
Excellent communication skills and the ability to work cross-functionally with Product, Engineering, Analytics, and other stakeholders.
Curiosity, ownership, and a passion for building products that customers love.

Nice To Haves

Experience with large-scale recommendation, ranking, personalization, or adaptive optimization systems.
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.
Experience serving large-scale ML models in production.
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 tools.

Responsibilities

Develop machine learning models that move beyond experimentation and into production, where they directly improve creator workflows and product experiences.
Help develop intelligent systems that improve how creators discover insights, make decisions, and create content, working alongside Analytics, Product, and Engineering.
Design, train, evaluate, optimize, and deploy production machine learning models.
Build recommendation, ranking, and personalization systems that adapt to creator behavior, product feedback, and changing objectives.
Apply reinforcement learning, contextual bandits, online learning, and other adaptive learning approaches where they improve product outcomes.
Design systems that balance exploration and exploitation, short-term performance and long-term value, and multiple competing product objectives.
Develop reward models, feedback models, and objective functions that translate noisy, sparse, delayed, or implicit signals into reliable model training and evaluation targets.
Work with logged interaction data to understand user behavior, evaluate model performance, improve decision quality, and reduce bias in model evaluation.
Apply offline policy evaluation, counterfactual evaluation, causal inference, or related techniques to reason about model changes before and after deployment.
Design experiments to evaluate model performance, measure product impact, and continuously improve production systems.
Build scalable model training, evaluation, deployment, and inference pipelines.
Optimize models for accuracy, latency, scalability, reliability, and production maintainability.
Work with structured and unstructured datasets using Python and SQL.
Collaborate closely with Product and Engineering to translate customer problems into machine learning solutions.
Stay current with advances in reinforcement learning, recommendation systems, ranking, personalization, deep learning, experimentation, and production ML, and thoughtfully apply new techniques where they create measurable value.