Machine Learning Scientist
$167,000–$185,000 year
On-siteCulver City, California, United States
Job Summary
Machine Learning Scientist to design, train, evaluate, optimize, and deploy production ML models across recommendation, ranking, and personalization systems. Responsibilities include building models that adapt to user behavior using reinforcement learning, contextual bandits, online learning, and adaptive decision-making; designing systems balancing short-term and long-term value; developing reward and feedback models; leveraging logged interaction data to understand user behavior, evaluate model performance, reduce bias in evaluation, and conduct offline policy evaluation and causal inference; building scalable training, evaluation, deployment, and inference pipelines; collaborating with Product and Engineering to translate customer problems into ML solutions; staying current with advances in RL, ranking, personalization, and production ML and applying them to create measurable value for creators. Requirements include a Master’s degree or PhD in a quantitative field, 5+ years of production ML experience, strong Python and SQL skills, and excellent cross-functional communication. Nice-to-haves cover large-scale systems experience, ad/campaign optimization familiarity, and production-scale deployment expertise. Location is Culver City, CA, with in-person work; compensation range disclosed for Culver City site; Spotter emphasizes equal opportunity employment and a culture of inclusion.
Required Qualifications
- 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
- Strong experience with modern deep learning frameworks and production ML workflows
- Experience with one or more of: recommendation systems, ranking systems, personalization models, reinforcement learning systems, contextual bandits, online learning systems, adaptive decision-making systems
- Strong understanding of reinforcement learning concepts (exploration vs. exploitation, reward design, delayed feedback, sequential decision-making)
- Experience working with logged interaction data or feedback signals to train and improve models
- Experience designing experiments and evaluating model performance in real-world product environments
- Experience with offline evaluation, counterfactual reasoning, causal inference, or related methods
- Experience training, evaluating, tuning, and deploying ML models across deep learning and traditional ML approaches
- Strong understanding of embeddings, representation learning, neural networks, sequence modeling, and modern DL architectures
- Strong Python and SQL skills
- Excellent communication skills and cross-functional collaboration
- Curiosity, ownership, and passion for building products customers love
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