Senior Software Engineer, Machine Learning

About the role

We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.)

Summary

At ZipRecruiter, where a universe of data, brimming with over a billion archived job postings, tens of millions of dynamic job seekers, and countless impression and click events, there's plenty of opportunities for innovation. Here, your experience will be put to work crafting new, data-driven features that impact the lives of millions, connecting them to their dream jobs.

We're tackling exciting challenges: imagine developing a system that can predict salaries for new job postings, using a training set of job postings and known salaries. But the fun doesn't stop at model-building. Our work involves an immersive dive into data - gathering, cleaning, analyzing, and extracting meaningful insights.

Key Focuses

• 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

Minimum Qualifications

• 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

Preferred Qualifications

• 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