Founding Engineer - ML

Datawizz helps companies reduce LLM costs by 85% while improving accuracy by over 20% by combining distillation, model routing, and pruning to route requests to smaller, more efficient models. We started in 2025 with the mission of making AI efficient, affordable and more accurate than ever before.

Datawizz sits between the application and the LLM, automatically logging requests, evaluating them on different models, and training custom SLMs for repeated tasks. Datawizz then automatically routes every request to the best model - significantly reducing costs and improving accuracy.

As a founding ML engineer, you’ll build the core intelligence layer of our platform. You’ll work on cutting-edge problems in evaluation, routing, fine-tuning and model distillation - turning research ideas into production systems at scale. We strive to leverage and productize the latest research, while pushing the boundaries in specific areas where we have unique customer exposure with our own research. This role will include opportunities for publishable research alongside product work.

You will:

• Design and build our model evaluation framework for measuring accuracy, latency, and cost across diverse models.

• Develop and productionize the SLM training pipeline, including data processing, training orchestration, and evaluation loops.

• Build and optimize our real-time LLM router to decide which model to use for each request.

• Contribute to infrastructure that enables rapid experimentation and model iteration at scale.

• Influence technical direction and help shape the culture of the engineering team.

• This role is in-office, 5 days/week, based in San Francisco.

We’re looking for builders who are excited to push the boundaries of efficient AI. You should be comfortable moving quickly, owning big pieces of the stack, and learning fast.

• Training and evaluating ML models (especially LLMs) using Python, PyTorch, Transformers, TRL, Unsloth etc.

• Designing experiments and building metrics/evaluation pipelines

• Scaling ML systems from prototype to production

• Deploying and operating ML workloads in the cloud (AWS, Kubernetes, Docker, etc.)

• Thriving in fast-paced startup environments with high ownership

• Competitive salary, based on experience level (Annual compensation range: $50,000-$500,000)

• Meaningful equity

• Opportunity to be a founding member of a growing company