S
$167,000 – $185,000
Listed on Spotter’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role builds machine learning systems that help YouTube creators optimize their content and business decisions, emphasizing production reinforcement learning, bandit algorithms, and recommendation systems. It's suited to experienced ML scientists who thrive shipping models to real users and iterating based on production feedback.
Our summary, not Spotter’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- Master's degree or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or related quantitative field
- 5+ years building, evaluating, and deploying machine learning models in production
- Experience with reinforcement learning or contextual bandit systems from coursework, research, or industry work
- Understanding of core RL training objectives including temporal-difference losses, policy gradient objectives, and clipped surrogate objectives
- Practical experience with bandit and RL methods such as Thompson sampling, UCB, LinUCB, policy gradients, or Q-learning
- Ability to design reward functions and diagnose reward hacking and feedback loops
- Knowledge of off-policy evaluation techniques like inverse propensity scoring and doubly robust estimators
- Experience with logged interaction data for training and evaluating models
- Track record of designing experiments and A/B testing in production environments
- Strong deep learning framework and production ML workflow experience
- Expertise training and deploying models across deep learning and traditional ML
- Understanding of embeddings, representation learning, and modern deep learning architectures
- Strong Python and SQL skills
- Excellent cross-functional communication skills
Nice to have
- Hands-on work building large-scale recommendation, ranking, or personalization systems
- Understanding of offline reinforcement learning methods like CQL or IQL
- Knowledge of constrained or safe reinforcement learning and guardrailed deployment
- Familiarity with ad recommendation or ranking systems at scale from YouTube, Google, Meta, TikTok, Amazon, or similar platforms
- Experience serving large-scale ML models in production
- Background building ML systems for creator platforms, consumer apps, recommendation systems, or workflow automation tools