ML Engineer - Full Lifecycle Model Training & Evaluation

DevExplore; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Zof AI is seeking a Machine Learning Engineer in San Francisco to own the full model lifecycle, from data gathering to production monitoring. You will train, fine-tune, and evaluate models for production use, build datasets and data pipelines, and own the training infrastructure and experiment tracking.

Ideal candidates have hands-on experience, a strong ML foundation, and the ability to communicate clearly in a fast-moving environment; experience shipping models or rigorous applied work is

Qualifications

• Hands-on experience training and fine-tuning models.
• Strong 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
• Make architectural decisions on 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 gathering through production monitoring

Skills

Python

ML training

Data pipelines

Model evaluation

Experiment tracking

Software engineering

Communication

Tools

PyTorch

JAX

Model serving

Job description

Zof AI is seeking a Machine Learning Engineer in San Francisco to own the full model lifecycle, from data gathering to production monitoring. You will train, fine-tune, and evaluate models for production use, build datasets and data pipelines, and own the training infrastructure and experiment tracking.

Ideal candidates have hands-on experience, a strong ML foundation, and the ability to communicate clearly in a fast-moving environment; experience shipping models or rigorous applied work is