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.