Senior · Staff · Principal Machine Learning Engineer

Senior / Staff / Principal Machine Learning Engineer Location: Onsite San Francisco (5 days onsite AND hybrid options) We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization. Key Responsibilities: Model Development: Designing and implementing ML algorithms and models, including deep learning models. Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation. System Integration: Collaborating with software engineers to integrate ML models into production systems. Performance Optimization: Continuously improving and optimizing ML models for accuracy, efficiency, and scalability. Monitoring and Maintenance: Monitoring model performance in production, troubleshooting issues, and ensuring model reliability. Staying Updated: Keeping abreast of the latest advancements in ML, AI, and related technologies. Collaboration: Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions. Essential Skills: Programming Languages: Strong proficiency in Python, R, or other relevant languages. ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms. Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges. Communication: Effective communication skills to convey technical information to both technical and non-technical audiences. Collaboration: Ability to work effectively in a team environment. Education and Experience: A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required. Several years of experience in machine learning, data science, or software development is often preferred. Compensation: Market range and can include equity – details can be provided after the specific client is determined.