> Markdown version of [/jobs/ext/588498-machine-learning-scientist](https://www.wearedevelopers.com/jobs/ext/588498-machine-learning-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Scientist - **Company:** Spotter, Inc - **Location:** Culver City, CA, United States - **Experience:** Expert - **Salary:** $185,000.0 - **Contract:** Franchise - **Skills:** A/B Testing, Data Analysis, Artificial Neural Networks, Python (Programming Language), Machine Learning, Recommender Systems, Standard Sql, SQL Databases, Web Platforms, Reinforcement Learning, Deep Learning, Model Validation, Information Technology, Low Latency, Machine Learning Operations - **Published:** June 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=61de00d2afc7d4a7 ## About the Role Do you have experience in Python?, Do you have a Master's degree?, * 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 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. ## Description 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 techniques where they create measurable value. ## Related Videos - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Beyond Autocomplete: Local AI Code Completion Demystified](https://www.wearedevelopers.com/videos/961-beyond-autocomplete-local-ai-code-completion-demystified) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career)