Machine Learning Scientist

Overview

Spotter empowers the worlds 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 725000 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.

Overview

Were 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 dont just perform well in experiments they ship to production and create real value for YouTube creators.

In this role youll 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. Youre 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.

Were 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 worlds top YouTube creators work smarter and create better content youll thrive at Spotter.

What Youll Do

Youll 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 youll 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 techniques where they create measurable value.

Who You Are

• Masters 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 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.

Why Spotter

• 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.

In compliance with locallaw 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 Spotters total compensation package for employees. Other rewards may include an annual discretionary bonus and equity.

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.