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# Software Engineer, Machine Learning

- **Company:** ZipRecruiter, Inc.
- **Location:** Santa Monica, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Cluster Analysis, Data Structures, Linear Regression, Machine Learning, Natural Language Processing, Named Entity Recognition, NLTK (NLP Analysis), Object-Oriented Software Development, Tensorflow, Sentiment Analysis, Software Engineering, Stemming, Support Vector Machine, Pytorch, Deep Learning, Generative AI, Convolutional Neural Networks, Scikit Learn, Information Technology, Feature Selection, Spacy, Recurrent Neural Networks
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/1bd2df8f-5748-4f48-a27d-62765426dd5e

## About the Role

* 3+ year of professional software development experience with a focus in machine learning
 * Deep experience in machine learning algorithms, techniques, and best practices
 * Comprehensive computer science fundamentals in coding, object-oriented programming, data structures, and algorithms, * 5+ year of professional software development experience with a focus in machine learning
 * BS/MS/PhD in Mathematics, Computer Science, Physics, related technical field or equivalent practical experience
 * Strong knowledge of machine learning algorithms (e.g., linear regression, SVM, decision trees, neural networks, clustering, etc.) and best practices
 * Experience with machine learning algorithms and frameworks, such as TensorFlow, PyTorch, or scikit-learn
 * Experience with deep learning architectures and techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GANs)
 * Background with NLP techniques and tools, such as tokenization, stemming, lemmatization, sentiment analysis, and named entity recognition, and libraries like NLTK, SpaCy, or BERT

## Description

* Design, develop, and maintain machine learning models and algorithms to solve complex business problems
 * Identify patterns, trends, and anomalies in the data, and visualize insights using appropriate tools
 * Assess the performance of machine learning models using appropriate metrics, validation techniques, and testing datasets
 * Discover opportunities to optimize models by fine-tuning hyperparameters, feature selection, or employing regularization techniques to improve accuracy, performance, and scalability


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