Machine Learning Engineer

Multi Media LLC

6d•$180,000 - $200,000•Remote

About The Position

Multi Media LLC is the company behind Chaturbate, one of the most heavily trafficked live streaming platforms in the world. We support a global network of independent content creators and millions of real-time viewers, delivering interactive video at scale. Our infrastructure handles complex broadcasting, low-latency streaming, and high-engagement user experiences. All live, all the time. We’re building a platform where creators can express themselves freely and grow their communities, where viewers discover and interact with creators they’re drawn to, and where the team behind it is challenged, trusted, and responsible for shaping the experience of millions of users around the world. We value people who take initiative, stay curious, and care deeply about the quality and impact of what they build. We are looking for a Machine Learning Engineer to join our Artificial Intelligence and Machine Learning team. In this role, you will help build, maintain, and improve production grade machine learning systems that support search, recommendations, personalization, computer vision, predictive modeling, and AI-driven experimentation across a global-scale consumer platform. This is a hands-on engineering role for someone who has experience building ML models and systems at scale, understands how to evaluate model performance in production contexts, and is excited to apply machine learning to practical product and business problems. You’ll partner closely with Data Scientists, Product, Engineering, and other cross-functional partners to translate product and business needs into scalable ML solutions that improve discovery, engagement, retention, and user experience.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a similar technical field, or equivalent practical experience.
3+ years of experience building, deploying, or maintaining machine learning systems in production environments.
Experience building ML models or systems at scale in areas such as search, recommender systems, personalization, computer vision, predictive modeling, or user-facing ranking systems.
Experience working on consumer-facing products where machine learning directly impacts user discovery, engagement, retention, or personalization.
Experience working with global-scale systems, high-traffic environments, or large-scale user behavior data.
Excellent Python programming skills.
Experience with common machine learning libraries, frameworks, and tooling, such as scikit-learn, PyTorch, TensorFlow, XGBoost, pandas, NumPy, or similar tools.
Ability to reason through ML system design, including data quality, model evaluation, performance tradeoffs, scalability, reliability, and monitoring.
Strong communication skills and experience partnering with Product, Engineering, Data Science, and other cross-functional partners to deliver practical ML solutions for complex product systems.

Nice To Haves

Experience with search systems, ranking systems, recommendation engines, embeddings, or information retrieval.

Responsibilities

Build, maintain, and improve production machine learning systems that support search, recommendations, personalization, computer vision, and predictive modeling.
Contribute to search and discovery improvements, including ranking, filtering, relevance, exact match, boolean logic, and LLM-powered enhancements.
Develop and integrate machine learning models that improve recommendation quality, search accuracy, behavioral analytics, and personalized user experiences.
Write clean, reliable, and maintainable code for ML pipelines, model development, experimentation, and production workflows.
Work with large-scale datasets to train, evaluate, monitor, and improve ML systems.
Collaborate with Data Science, Product, Engineering, and other cross-functional partners to understand requirements, evaluate tradeoffs, and deliver ML solutions that create measurable product and business impact.
Participate in technical design discussions for ML systems, including model architecture, data pipelines, evaluation methods, deployment approaches, monitoring, and scalability.

Benefits

Fair and competitive base salary
Fully Remote Optional
Health, Vision, Dental, and Life Insurance for you and any dependents, with policy premiums covered by the Company
Long & Short term disability insurance
Unlimited PTO
Annual Year-End Company Closure
Optional 401k with 5% matching
12 Paid Holidays
Paid Lunches in-office, or if Remote, a $125/week stipend via Sharebite
Employee Assistance and Employee Recognition Programs