Machine Learning Scientist

About Spotter

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.

About the Role

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.

What You'll Do

• Develop machine learning models that move beyond experimentation and into production, where they directly improve creator workflows and product experiences.
• Work alongside Analytics, Product, and Engineering to help develop intelligent systems that improve how creators discover insights, make decisions, and create content.
• 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.

Who You Are

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

Nice to Have

• 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.

Benefits

• Medical insurance covered up to 100%
• Dental & vision insurance
• 401(k) matching
• Stock options
• Discretionary PTO
• Complimentary gym access
• Autonomy and upward mobility
• Diverse, equitable, and inclusive culture, where your voice matters.

Compensation

A reasonable estimate of the current pay range for roles performed in Culver City is: $167K–$185K salary per year. 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. 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.

Equal Opportunity

Spotter 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.

Equal 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.