Senior * Staff * Principal Machine Learning Engineer

About the role

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.