Member of Technical Staff, Machine Learning

About the Role

This is a hands-on machine learning engineering role at a fast-moving AI data solutions company, sitting at the intersection of multimodal data and frontier AI development. You will help build and ship ML models and systems that power high-quality video, audio, and image data pipelines used by leading AI research teams and enterprises. The work directly shapes the data infrastructure that enables next-generation generative and embodied AI.

What You'll Do

• Train, evaluate, and deploy machine learning models into production environments.

• Build and maintain data preprocessing, feature engineering, and exploratory data analysis pipelines.

• Work with large-scale multimodal datasets including video, audio, and image data.

• Collaborate with research and engineering teams to understand model needs, failure modes, and evaluation goals.

• Apply MLOps best practices including model monitoring and CI/CD for ML workflows.

• Write clean, maintainable Python code and manage work through version control.

What We're Looking For

• 3+ years of experience building and delivering ML models or systems in production.

• Strong proficiency in Python for machine learning development and data manipulation.

• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Solid understanding of data preprocessing, feature engineering, and exploratory data analysis.

• Experience with SQL and working with databases or data warehouses.

• Familiarity with version control systems such as Git.

• Experience with cloud ML platforms (e.g., AWS SageMaker, Google Cloud AI, or Azure ML) is a plus.

• Experience with deep learning and neural networks is a plus.

• Background at a top-tier technology or AI company is a strong signal.

Compensation & Benefits

Salary range: $150,000 to $350,000 USD annually, depending on experience. Equity participation is expected given the company stage. Please note that visa sponsorship is not available for this role.

Location

This is a full-time, on-site role based in San Francisco, California.