Machine Learning Scientist

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 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. For more information about Spotter, please visit .

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 You Tube 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 You Tube creators work smarter and create better content, you’ll thrive at Spotter.

You’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.

Your work may include:

• Designing, training, evaluating, optimizing, and deploying production machine learning models.
• Building recommendation, ranking, and personalization systems that adapt to creator behavior, product feedback, and changing objectives.
• Applying reinforcement learning, contextual bandits, online learning, and other adaptive learning approaches where they improve product outcomes.
• Designing systems that balance exploration and exploitation, short‑term performance and long‑term value, and multiple competing product objectives.
• Developing reward models, feedback models, and objective functions that translate noisy, sparse, delayed, or implicit signals into reliable model training and evaluation targets.
• Working with logged interaction data to understand user behavior, evaluate model performance, improve decision quality, and reduce bias in model evaluation.
• Applying offline policy evaluation, counterfactual evaluation, causal inference, or related techniques to reason about model changes before and after deployment.
• Designing experiments to evaluate model performance, measure product impact, and continuously improve production systems.
• Building scalable model training, evaluation, deployment, and inference pipelines.
• Optimizing models for accuracy, latency, scalability, reliability, and production maintainability.
• Working with structured and unstructured datasets using Python and SQL.
• Collaborating closely with Product and Engineering to translate customer problems into machine learning solutions.
• Staying current with advances in reinforcement learning, recommendation systems, ranking, personalization, deep learning, experimentation, and production ML, and thoughtfully applying new…