Senior · Staff · Principal Machine Learning Engineer
This role focuses on architecting and deploying advanced ML systems for high-growth startups, bridging algorithmic innovation with scalable engineering solutions. Candidates will lead model development while collaborating closely with cross-functional teams to embed AI into production workflows.
Skills required
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.
