Lead Allies Inc
Senior · Staff · Principal Machine Learning Engineer
Lead Allies Inc, San Francisco, California, United States, 94199
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.
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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.
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