Senior Machine Learning Engineer

Zof AI; San Francisco, Northern (CA, KY); Hybrid; USD 170,000 - 250,000; Full time

Job summary

Zof AI is seeking a Senior Machine Learning Engineer to own the end-to-end lifecycle of models—from data collection and labeling to training, evaluation, and production monitoring. The role emphasizes rigorous evaluation, pragmatic decision-making on when to train or call APIs, and close collaboration with AI and product engineers to ship features.

Ideal candidates will have hands-on experience with training and fine-tuning, a strong ML foundation, Python proficiency, and a track record of

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

Model fine-tuning

ML evaluation

Python

Data pipelines

Software engineering

Communication

Production models

Tools

PyTorch

Model serving

Embeddings

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