Machine Learning Engineer
New
M
Multi Media, LLCMachine Learning
United StatesFull-TimeMiddle
Salary$180,000 - $200,000 USD
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonMachine LearningNumpyPyTorchPandasTensorflowscikit-learnComputer Vision
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.
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.
- Participate in technical design discussions for ML systems, including model architecture, data pipelines, evaluation methods, deployment approaches, monitoring, and scalability.
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