Applied Machine Learning Engineer

Zof AI; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Zof AI in San Francisco, CA is seeking a Senior Machine Learning Engineer to own the full model lifecycle from data to production. You will train, fine-tune, and evaluate models for production use, build datasets and pipelines, and manage training infrastructure with rigorous evaluation practices.

The role emphasizes hands-on ML work, strong evaluation discipline, and collaboration with AI and product engineers to ship models into features on-site in a fast-paced environment.

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.

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

Model fine-tuning

Evaluation methodology

Python

Software engineering

Communication

Tools

PyTorch

JAX

TensorFlow

Experiment tracking

Job description

Zof AI is seeking a Senior Machine Learning Engineer for the traditional ML discipline: training, fine-tuning, and evaluating models rather than only building on top of them. This role owns datasets, training pipelines, and the applied models that power verification and quality systems inside our products. The ideal candidate is rigorous about evaluation, comfortable owning the full model lifecycle, and pragmatic about when a trained model beats an API call.

Engineering · Mid to Senior · Full-time · On-site · San Francisco, CA

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.

Requirements

• 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.

Nice to have

• Experience fine-tuning LLMs or working with embeddings at scale.
• Experience with PyTorch, JAX, or similar frameworks.
• Experience with model serving and inference optimization.
• Published work, competition results, or strong open-source contributions.

Hands-on experience training, fine-tuning, and evaluating machine learning models is required