Senior Machine Learning Engineer

Senior Machine Learning Engineer Job Description

Job
Worker Sub-Type Regular
Remote Type Remote
Job Categories R&D

Senior Machine Learning Engineer Remote

Senior Machine Learning Engineer. Remote. Salary $90-160K/year.

• 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.

• 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 Tensor Flow, 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.

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