Senior · Staff · Principal Machine Learning Engineer

About this role

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.