Remote
Duties: Design, develop, and optimize AutoML algorithms and frameworks to automate model selection, hyperparameter tuning, feature engineering, and deployment workflows. Research, prototype, and integrate state-of-the-art machine learning and deep learning techniques into the AutoML framework. Collaborate cross-functionally with data scientists, software engineers, MLOps engineers, and domain experts to understand user requirements and translate them into scalable solutions. Build, test, and deploy machine learning models in production environments, ensuring robustness, scalability, and efficiency. Contribute to the design of end-to-end machine learning pipelines, including data ingestion, preprocessing, model training, evaluation, deployment, and monitoring. Implement model monitoring, drift detection, and retraining strategies to ensure sustained model performance over time. Optimize model performance, resource utilization, and inference speed for production use cases. Ensure adherence to best practices in software engineering, including code reviews, version control, testing, and documentation. Stay up to date with industry trends, academic research, and emerging technologies in machine learning, AutoML, and AI infrastructure. Mentor and provide technical guidance to junior team members. May work remotely from anywhere in the U.S. Salary $90-160K/year.
Requirements: Master’s degree in Electrical Engineering, Computer Science, Machine Learning, Data Science, or a related field. 2 years of experience (before/during/after degree) as Test Engineer, Machine Learning Engineer, Software Engineer or related. 6 months (may be before/during/after degree and concurrent with 2 years) in the following: Python and one additional language, such as Java, C++, Go, or comparable; Machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost or comparable; Fine-tuning large language models (GPT-3, Jurassic-Jumbo, GPT-J) on GCP and Google Colab and deploying them into production for tasks such as text completion and summarization; Building production-quality machine learning software using BERT or comparable LLM; Statistical and mathematical concepts related to machine learning; Working with cloud platforms and distributed computing technologies such as AWS and Google Cloud.