Machine Learning Engineer

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

About The Role

Zof AI is seeking a 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.

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

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 in Machine Learning, Model Training, Fine-Tuning, Datasets, or Evaluation