Senior Applied ML Engineer — On-Site in SF, with Equity

Zof AI, Inc.; San Francisco (CA); On-site; USD 84,000 - 167,000; Full time

Benefits offered by this job

MacBook Pro

Premium AI development tools

High ownership

Direct product impact

Close collaboration with leadership

Job summary

Zof AI, Inc. is seeking a Senior Applied Machine Learning Engineer in San Francisco to own the full model lifecycle—from data preparation to production monitoring.

You will train, fine-tune, and evaluate models for real-world deployment, building robust data pipelines and scalable training infrastructure. Candidate must demonstrate hands-on experience with ML fundamentals, evaluation methodologies, and strong software engineering discipline, plus collaborative work with engineers to ship models

Qualifications

• Hands-on experience training and fine-tuning models.
• Strong grounding in ML fundamentals and evaluation methodology.
• Experience with Python and the modern ML stack.
• Experience building datasets and data pipelines.
• Strong software engineering discipline.
• Clear written and verbal communication.
• Comfort operating in a fast-moving environment.
• Evidence of models shipped to production or rigorous applied work.

Responsibilities

• Train, fine-tune, and evaluate models for production use.
• Build and maintain datasets, labeling pipelines, and data quality checks.
• Own training infrastructure and experiment tracking.
• Develop applied models that power verification and quality features.
• Establish rigorous evaluation and regression testing for models.
• Decide when to train, when to fine-tune, and when to call an API.
• Collaborate with AI and product engineers to ship models into features.
• Own the model lifecycle from data through production monitoring.

Skills

Model training

Fine-tuning

Python

ML evaluation

Data pipelines

Software engineering

Communication

Production ML

Model lifecycle

Tools

PyTorch

JAX

Job description

Zof AI, Inc. is seeking a Senior Applied Machine Learning Engineer in San Francisco to own the full model lifecycle—from data preparation to production monitoring.

You will train, fine-tune, and evaluate models for real-world deployment, building robust data pipelines and scalable training infrastructure. Candidate must demonstrate hands-on experience with ML fundamentals, evaluation methodologies, and strong software engineering discipline, plus collaborative work with engineers to ship models